WorldWideScience

Sample records for bias statistics

  1. Bias expansion of spatial statistics and approximation of differenced ...

    Indian Academy of Sciences (India)

    Investigations of spatial statistics, computed from lattice data in the plane, can lead to a special lattice point counting problem. The statistical goal is to expand the asymptotic expectation or large-sample bias of certain spatial covariance estimators, where this bias typically depends on the shape of a spatial sampling region.

  2. Statistical methods for accurately determining criticality code bias

    International Nuclear Information System (INIS)

    Trumble, E.F.; Kimball, K.D.

    1997-01-01

    A system of statistically treating validation calculations for the purpose of determining computer code bias is provided in this paper. The following statistical treatments are described: weighted regression analysis, lower tolerance limit, lower tolerance band, and lower confidence band. These methods meet the criticality code validation requirements of ANS 8.1. 8 refs., 5 figs., 4 tabs

  3. Reducing bias in the analysis of counting statistics data

    International Nuclear Information System (INIS)

    Hammersley, A.P.; Antoniadis, A.

    1997-01-01

    In the analysis of counting statistics data it is common practice to estimate the variance of the measured data points as the data points themselves. This practice introduces a bias into the results of further analysis which may be significant, and under certain circumstances lead to false conclusions. In the case of normal weighted least squares fitting this bias is quantified and methods to avoid it are proposed. (orig.)

  4. New method for eliminating the statistical bias in highly turbulent flow measurements

    International Nuclear Information System (INIS)

    Nakao, S.I.; Terao, Y.; Hirata, K.I.; Kitakyushu Industrial Research Institute, Fukuoka, Japan)

    1987-01-01

    A simple method was developed for eliminating statistical bias which can be applied to highly turbulent flows with the sparse and nonuniform seeding conditions. Unlike the method proposed so far, a weighting function was determined based on the idea that the statistical bias could be eliminated if the asymmetric form of the probability density function of the velocity data were corrected. Moreover, the data more than three standard deviations away from the mean were discarded to remove the apparent turbulent intensity resulting from noise. The present method was applied to data obtained in the wake of a block, which provided local turbulent intensities up to about 120 percent, it was found to eliminate the statistical bias with high accuracy. 9 references

  5. Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model

    Energy Technology Data Exchange (ETDEWEB)

    Walker, M D; Asselin, M-C; Julyan, P J; Feldmann, M; Matthews, J C [School of Cancer and Enabling Sciences, Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Talbot, P S [Mental Health and Neurodegeneration Research Group, Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Jones, T, E-mail: matthew.walker@manchester.ac.uk [Academic Department of Radiation Oncology, Christie Hospital, University of Manchester, Manchester M20 4BX (United Kingdom)

    2011-02-21

    Iterative image reconstruction methods such as ordered-subset expectation maximization (OSEM) are widely used in PET. Reconstructions via OSEM are however reported to be biased for low-count data. We investigated this and considered the impact for dynamic PET. Patient listmode data were acquired in [{sup 11}C]DASB and [{sup 15}O]H{sub 2}O scans on the HRRT brain PET scanner. These data were subsampled to create many independent, low-count replicates. The data were reconstructed and the images from low-count data were compared to the high-count originals (from the same reconstruction method). This comparison enabled low-statistics bias to be calculated for the given reconstruction, as a function of the noise-equivalent counts (NEC). Two iterative reconstruction methods were tested, one with and one without an image-based resolution model (RM). Significant bias was observed when reconstructing data of low statistical quality, for both subsampled human and simulated data. For human data, this bias was substantially reduced by including a RM. For [{sup 11}C]DASB the low-statistics bias in the caudate head at 1.7 M NEC (approx. 30 s) was -5.5% and -13% with and without RM, respectively. We predicted biases in the binding potential of -4% and -10%. For quantification of cerebral blood flow for the whole-brain grey- or white-matter, using [{sup 15}O]H{sub 2}O and the PET autoradiographic method, a low-statistics bias of <2.5% and <4% was predicted for reconstruction with and without the RM. The use of a resolution model reduces low-statistics bias and can hence be beneficial for quantitative dynamic PET.

  6. A question of separation: disentangling tracer bias and gravitational non-linearity with counts-in-cells statistics

    Science.gov (United States)

    Uhlemann, C.; Feix, M.; Codis, S.; Pichon, C.; Bernardeau, F.; L'Huillier, B.; Kim, J.; Hong, S. E.; Laigle, C.; Park, C.; Shin, J.; Pogosyan, D.

    2018-02-01

    Starting from a very accurate model for density-in-cells statistics of dark matter based on large deviation theory, a bias model for the tracer density in spheres is formulated. It adopts a mean bias relation based on a quadratic bias model to relate the log-densities of dark matter to those of mass-weighted dark haloes in real and redshift space. The validity of the parametrized bias model is established using a parametrization-independent extraction of the bias function. This average bias model is then combined with the dark matter PDF, neglecting any scatter around it: it nevertheless yields an excellent model for densities-in-cells statistics of mass tracers that is parametrized in terms of the underlying dark matter variance and three bias parameters. The procedure is validated on measurements of both the one- and two-point statistics of subhalo densities in the state-of-the-art Horizon Run 4 simulation showing excellent agreement for measured dark matter variance and bias parameters. Finally, it is demonstrated that this formalism allows for a joint estimation of the non-linear dark matter variance and the bias parameters using solely the statistics of subhaloes. Having verified that galaxy counts in hydrodynamical simulations sampled on a scale of 10 Mpc h-1 closely resemble those of subhaloes, this work provides important steps towards making theoretical predictions for density-in-cells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST.

  7. Detecting rater bias using a person-fit statistic: a Monte Carlo simulation study.

    Science.gov (United States)

    Aubin, André-Sébastien; St-Onge, Christina; Renaud, Jean-Sébastien

    2018-04-01

    With the Standards voicing concern for the appropriateness of response processes, we need to explore strategies that would allow us to identify inappropriate rater response processes. Although certain statistics can be used to help detect rater bias, their use is complicated by either a lack of data about their actual power to detect rater bias or the difficulty related to their application in the context of health professions education. This exploratory study aimed to establish the worthiness of pursuing the use of l z to detect rater bias. We conducted a Monte Carlo simulation study to investigate the power of a specific detection statistic, that is: the standardized likelihood l z person-fit statistics (PFS). Our primary outcome was the detection rate of biased raters, namely: raters whom we manipulated into being either stringent (giving lower scores) or lenient (giving higher scores), using the l z statistic while controlling for the number of biased raters in a sample (6 levels) and the rate of bias per rater (6 levels). Overall, stringent raters (M = 0.84, SD = 0.23) were easier to detect than lenient raters (M = 0.31, SD = 0.28). More biased raters were easier to detect then less biased raters (60% bias: 62, SD = 0.37; 10% bias: 43, SD = 0.36). The PFS l z seems to offer an interesting potential to identify biased raters. We observed detection rates as high as 90% for stringent raters, for whom we manipulated more than half their checklist. Although we observed very interesting results, we cannot generalize these results to the use of PFS with estimated item/station parameters or real data. Such studies should be conducted to assess the feasibility of using PFS to identify rater bias.

  8. Incorporating circulation statistics in bias correction of GCM ensembles: Hydrological application for the Rhine basin

    NARCIS (Netherlands)

    Photiadou, C.; van den Hurk, B.J.J.M.; Delden, A. van; Weerts, A.

    2016-01-01

    An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a historical period. The bias correction method is tailored to time scales relevant to

  9. Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin

    NARCIS (Netherlands)

    Photiadou, C.; Hurk, van den B.; Delden, van A.; Weerts, A.H.

    2016-01-01

    An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a historical period. The bias correction method is tailored to time scales relevant to

  10. Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin

    NARCIS (Netherlands)

    Photiadou, Christiana; van den Hurk, Bart; van Delden, Aarnout; Weerts, Albrecht

    2015-01-01

    An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a histori- cal period. The bias correction method is tailored to time scales relevant to

  11. Possible Solution to Publication Bias Through Bayesian Statistics, Including Proper Null Hypothesis Testing

    NARCIS (Netherlands)

    Konijn, Elly A.; van de Schoot, Rens; Winter, Sonja D.; Ferguson, Christopher J.

    2015-01-01

    The present paper argues that an important cause of publication bias resides in traditional frequentist statistics forcing binary decisions. An alternative approach through Bayesian statistics provides various degrees of support for any hypothesis allowing balanced decisions and proper null

  12. Examining publication bias—a simulation-based evaluation of statistical tests on publication bias

    Directory of Open Access Journals (Sweden)

    Andreas Schneck

    2017-11-01

    Full Text Available Background Publication bias is a form of scientific misconduct. It threatens the validity of research results and the credibility of science. Although several tests on publication bias exist, no in-depth evaluations are available that examine which test performs best for different research settings. Methods Four tests on publication bias, Egger’s test (FAT, p-uniform, the test of excess significance (TES, as well as the caliper test, were evaluated in a Monte Carlo simulation. Two different types of publication bias and its degree (0%, 50%, 100% were simulated. The type of publication bias was defined either as file-drawer, meaning the repeated analysis of new datasets, or p-hacking, meaning the inclusion of covariates in order to obtain a significant result. In addition, the underlying effect (β = 0, 0.5, 1, 1.5, effect heterogeneity, the number of observations in the simulated primary studies (N = 100, 500, and the number of observations for the publication bias tests (K = 100, 1,000 were varied. Results All tests evaluated were able to identify publication bias both in the file-drawer and p-hacking condition. The false positive rates were, with the exception of the 15%- and 20%-caliper test, unbiased. The FAT had the largest statistical power in the file-drawer conditions, whereas under p-hacking the TES was, except under effect heterogeneity, slightly better. The CTs were, however, inferior to the other tests under effect homogeneity and had a decent statistical power only in conditions with 1,000 primary studies. Discussion The FAT is recommended as a test for publication bias in standard meta-analyses with no or only small effect heterogeneity. If two-sided publication bias is suspected as well as under p-hacking the TES is the first alternative to the FAT. The 5%-caliper test is recommended under conditions of effect heterogeneity and a large number of primary studies, which may be found if publication bias is examined in a

  13. Symmetries, invariants and generating functions: higher-order statistics of biased tracers

    Science.gov (United States)

    Munshi, Dipak

    2018-01-01

    Gravitationally collapsed objects are known to be biased tracers of an underlying density contrast. Using symmetry arguments, generalised biasing schemes have recently been developed to relate the halo density contrast δh with the underlying density contrast δ, divergence of velocity θ and their higher-order derivatives. This is done by constructing invariants such as s, t, ψ,η. We show how the generating function formalism in Eulerian standard perturbation theory (SPT) can be used to show that many of the additional terms based on extended Galilean and Lifshitz symmetry actually do not make any contribution to the higher-order statistics of biased tracers. Other terms can also be drastically simplified allowing us to write the vertices associated with δh in terms of the vertices of δ and θ, the higher-order derivatives and the bias coefficients. We also compute the cumulant correlators (CCs) for two different tracer populations. These perturbative results are valid for tree-level contributions but at an arbitrary order. We also take into account the stochastic nature bias in our analysis. Extending previous results of a local polynomial model of bias, we express the one-point cumulants Script SN and their two-point counterparts, the CCs i.e. Script Cpq, of biased tracers in terms of that of their underlying density contrast counterparts. As a by-product of our calculation we also discuss the results using approximations based on Lagrangian perturbation theory (LPT).

  14. Statistical methods for elimination of guarantee-time bias in cohort studies: a simulation study

    Directory of Open Access Journals (Sweden)

    In Sung Cho

    2017-08-01

    Full Text Available Abstract Background Aspirin has been considered to be beneficial in preventing cardiovascular diseases and cancer. Several pharmaco-epidemiology cohort studies have shown protective effects of aspirin on diseases using various statistical methods, with the Cox regression model being the most commonly used approach. However, there are some inherent limitations to the conventional Cox regression approach such as guarantee-time bias, resulting in an overestimation of the drug effect. To overcome such limitations, alternative approaches, such as the time-dependent Cox model and landmark methods have been proposed. This study aimed to compare the performance of three methods: Cox regression, time-dependent Cox model and landmark method with different landmark times in order to address the problem of guarantee-time bias. Methods Through statistical modeling and simulation studies, the performance of the above three methods were assessed in terms of type I error, bias, power, and mean squared error (MSE. In addition, the three statistical approaches were applied to a real data example from the Korean National Health Insurance Database. Effect of cumulative rosiglitazone dose on the risk of hepatocellular carcinoma was used as an example for illustration. Results In the simulated data, time-dependent Cox regression outperformed the landmark method in terms of bias and mean squared error but the type I error rates were similar. The results from real-data example showed the same patterns as the simulation findings. Conclusions While both time-dependent Cox regression model and landmark analysis are useful in resolving the problem of guarantee-time bias, time-dependent Cox regression is the most appropriate method for analyzing cumulative dose effects in pharmaco-epidemiological studies.

  15. Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

    Science.gov (United States)

    Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

    2013-01-01

    Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

  16. Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance

    Directory of Open Access Journals (Sweden)

    Zhang Zhang

    2012-03-01

    Full Text Available Abstract Background Genetic mutation, selective pressure for translational efficiency and accuracy, level of gene expression, and protein function through natural selection are all believed to lead to codon usage bias (CUB. Therefore, informative measurement of CUB is of fundamental importance to making inferences regarding gene function and genome evolution. However, extant measures of CUB have not fully accounted for the quantitative effect of background nucleotide composition and have not statistically evaluated the significance of CUB in sequence analysis. Results Here we propose a novel measure--Codon Deviation Coefficient (CDC--that provides an informative measurement of CUB and its statistical significance without requiring any prior knowledge. Unlike previous measures, CDC estimates CUB by accounting for background nucleotide compositions tailored to codon positions and adopts the bootstrapping to assess the statistical significance of CUB for any given sequence. We evaluate CDC by examining its effectiveness on simulated sequences and empirical data and show that CDC outperforms extant measures by achieving a more informative estimation of CUB and its statistical significance. Conclusions As validated by both simulated and empirical data, CDC provides a highly informative quantification of CUB and its statistical significance, useful for determining comparative magnitudes and patterns of biased codon usage for genes or genomes with diverse sequence compositions.

  17. Codon Deviation Coefficient: A novel measure for estimating codon usage bias and its statistical significance

    KAUST Repository

    Zhang, Zhang

    2012-03-22

    Background: Genetic mutation, selective pressure for translational efficiency and accuracy, level of gene expression, and protein function through natural selection are all believed to lead to codon usage bias (CUB). Therefore, informative measurement of CUB is of fundamental importance to making inferences regarding gene function and genome evolution. However, extant measures of CUB have not fully accounted for the quantitative effect of background nucleotide composition and have not statistically evaluated the significance of CUB in sequence analysis.Results: Here we propose a novel measure--Codon Deviation Coefficient (CDC)--that provides an informative measurement of CUB and its statistical significance without requiring any prior knowledge. Unlike previous measures, CDC estimates CUB by accounting for background nucleotide compositions tailored to codon positions and adopts the bootstrapping to assess the statistical significance of CUB for any given sequence. We evaluate CDC by examining its effectiveness on simulated sequences and empirical data and show that CDC outperforms extant measures by achieving a more informative estimation of CUB and its statistical significance.Conclusions: As validated by both simulated and empirical data, CDC provides a highly informative quantification of CUB and its statistical significance, useful for determining comparative magnitudes and patterns of biased codon usage for genes or genomes with diverse sequence compositions. 2012 Zhang et al; licensee BioMed Central Ltd.

  18. Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study

    Science.gov (United States)

    2014-01-01

    Background Analysis of variance (ANOVA), change-score analysis (CSA) and analysis of covariance (ANCOVA) respond differently to baseline imbalance in randomized controlled trials. However, no empirical studies appear to have quantified the differential bias and precision of estimates derived from these methods of analysis, and their relative statistical power, in relation to combinations of levels of key trial characteristics. This simulation study therefore examined the relative bias, precision and statistical power of these three analyses using simulated trial data. Methods 126 hypothetical trial scenarios were evaluated (126 000 datasets), each with continuous data simulated by using a combination of levels of: treatment effect; pretest-posttest correlation; direction and magnitude of baseline imbalance. The bias, precision and power of each method of analysis were calculated for each scenario. Results Compared to the unbiased estimates produced by ANCOVA, both ANOVA and CSA are subject to bias, in relation to pretest-posttest correlation and the direction of baseline imbalance. Additionally, ANOVA and CSA are less precise than ANCOVA, especially when pretest-posttest correlation ≥ 0.3. When groups are balanced at baseline, ANCOVA is at least as powerful as the other analyses. Apparently greater power of ANOVA and CSA at certain imbalances is achieved in respect of a biased treatment effect. Conclusions Across a range of correlations between pre- and post-treatment scores and at varying levels and direction of baseline imbalance, ANCOVA remains the optimum statistical method for the analysis of continuous outcomes in RCTs, in terms of bias, precision and statistical power. PMID:24712304

  19. Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

    Science.gov (United States)

    Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

    2018-02-01

    Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

  20. Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study

    Directory of Open Access Journals (Sweden)

    Vickers Andrew J

    2008-11-01

    Full Text Available Abstract Background A common feature of diagnostic research is that results for a diagnostic gold standard are available primarily for patients who are positive for the test under investigation. Data from such studies are subject to what has been termed "verification bias". We evaluated statistical methods for verification bias correction when there are few false negatives. Methods A simulation study was conducted of a screening study subject to verification bias. We compared estimates of the area-under-the-curve (AUC corrected for verification bias varying both the rate and mechanism of verification. Results In a single simulated data set, varying false negatives from 0 to 4 led to verification bias corrected AUCs ranging from 0.550 to 0.852. Excess variation associated with low numbers of false negatives was confirmed in simulation studies and by analyses of published studies that incorporated verification bias correction. The 2.5th – 97.5th centile range constituted as much as 60% of the possible range of AUCs for some simulations. Conclusion Screening programs are designed such that there are few false negatives. Standard statistical methods for verification bias correction are inadequate in this circumstance.

  1. Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement

    Science.gov (United States)

    Azorin-Molina, Cesar; Asin, Jesus; McVicar, Tim R.; Minola, Lorenzo; Lopez-Moreno, Juan I.; Vicente-Serrano, Sergio M.; Chen, Deliang

    2018-05-01

    Recent studies on observed wind variability have revealed a decline (termed "stilling") of near-surface wind speed during the last 30-50 years over many mid-latitude terrestrial regions, particularly in the Northern Hemisphere. The well-known impact of cup anemometer drift (i.e., wear on the bearings) on the observed weakening of wind speed has been mentioned as a potential contributor to the declining trend. However, to date, no research has quantified its contribution to stilling based on measurements, which is most likely due to lack of quantification of the ageing effect. In this study, a 3-year field experiment (2014-2016) with 10-minute paired wind speed measurements from one new and one malfunctioned (i.e., old bearings) SEAC SV5 cup anemometer which has been used by the Spanish Meteorological Agency in automatic weather stations since mid-1980s, was developed for assessing for the first time the role of anemometer drift on wind speed measurement. The results showed a statistical significant impact of anemometer drift on wind speed measurements, with the old anemometer measuring lower wind speeds than the new one. Biases show a marked temporal pattern and clear dependency on wind speed, with both weak and strong winds causing significant biases. This pioneering quantification of biases has allowed us to define two regression models that correct up to 37% of the artificial bias in wind speed due to measurement with an old anemometer.

  2. Large-scale galaxy bias

    Science.gov (United States)

    Desjacques, Vincent; Jeong, Donghui; Schmidt, Fabian

    2018-02-01

    This review presents a comprehensive overview of galaxy bias, that is, the statistical relation between the distribution of galaxies and matter. We focus on large scales where cosmic density fields are quasi-linear. On these scales, the clustering of galaxies can be described by a perturbative bias expansion, and the complicated physics of galaxy formation is absorbed by a finite set of coefficients of the expansion, called bias parameters. The review begins with a detailed derivation of this very important result, which forms the basis of the rigorous perturbative description of galaxy clustering, under the assumptions of General Relativity and Gaussian, adiabatic initial conditions. Key components of the bias expansion are all leading local gravitational observables, which include the matter density but also tidal fields and their time derivatives. We hence expand the definition of local bias to encompass all these contributions. This derivation is followed by a presentation of the peak-background split in its general form, which elucidates the physical meaning of the bias parameters, and a detailed description of the connection between bias parameters and galaxy statistics. We then review the excursion-set formalism and peak theory which provide predictions for the values of the bias parameters. In the remainder of the review, we consider the generalizations of galaxy bias required in the presence of various types of cosmological physics that go beyond pressureless matter with adiabatic, Gaussian initial conditions: primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature perturbations, dark energy, and modified gravity. Finally, we discuss how the description of galaxy bias in the galaxies' rest frame is related to clustering statistics measured from the observed angular positions and redshifts in actual galaxy catalogs.

  3. Sympathetic bias.

    Science.gov (United States)

    Levy, David M; Peart, Sandra J

    2008-06-01

    We wish to deal with investigator bias in a statistical context. We sketch how a textbook solution to the problem of "outliers" which avoids one sort of investigator bias, creates the temptation for another sort. We write down a model of the approbation seeking statistician who is tempted by sympathy for client to violate the disciplinary standards. We give a simple account of one context in which we might expect investigator bias to flourish. Finally, we offer tentative suggestions to deal with the problem of investigator bias which follow from our account. As we have given a very sparse and stylized account of investigator bias, we ask what might be done to overcome this limitation.

  4. Statistical study on the self-selection bias in FDG-PET cancer screening by a questionnaire survey

    International Nuclear Information System (INIS)

    Kita, Tamotsu; Yano, Fuzuki; Watanabe, Sadahiro; Soga, Shigeyoshi; Hama, Yukihiro; Shinmoto, Hiroshi; Kosuda, Shigeru

    2008-01-01

    A questionnaire survey was performed to investigate the possible presence of self-selection bias in 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) cancer screening (PET cancer screening). Responders to the questionnaires survey consisted of 80 healthy persons, who answered whether they undergo PET cancer screening, health consciousness, age, sex, and smoking history. The univariate and multivariate analyses on the four parameters were performed between the responders who were to undergo PET cancer screening and the responders who were not. Statistically significant difference was found in health consciousness between the above-mentioned two groups by both univariate and multivariate analysis with the odds ratio of 2.088. The study indicated that self-selection bias should exist in PET cancer screening. (author)

  5. Large-scale galaxy bias

    Science.gov (United States)

    Jeong, Donghui; Desjacques, Vincent; Schmidt, Fabian

    2018-01-01

    Here, we briefly introduce the key results of the recent review (arXiv:1611.09787), whose abstract is as following. This review presents a comprehensive overview of galaxy bias, that is, the statistical relation between the distribution of galaxies and matter. We focus on large scales where cosmic density fields are quasi-linear. On these scales, the clustering of galaxies can be described by a perturbative bias expansion, and the complicated physics of galaxy formation is absorbed by a finite set of coefficients of the expansion, called bias parameters. The review begins with a detailed derivation of this very important result, which forms the basis of the rigorous perturbative description of galaxy clustering, under the assumptions of General Relativity and Gaussian, adiabatic initial conditions. Key components of the bias expansion are all leading local gravitational observables, which include the matter density but also tidal fields and their time derivatives. We hence expand the definition of local bias to encompass all these contributions. This derivation is followed by a presentation of the peak-background split in its general form, which elucidates the physical meaning of the bias parameters, and a detailed description of the connection between bias parameters and galaxy (or halo) statistics. We then review the excursion set formalism and peak theory which provide predictions for the values of the bias parameters. In the remainder of the review, we consider the generalizations of galaxy bias required in the presence of various types of cosmological physics that go beyond pressureless matter with adiabatic, Gaussian initial conditions: primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature perturbations, dark energy, and modified gravity. Finally, we discuss how the description of galaxy bias in the galaxies' rest frame is related to clustering statistics measured from the observed angular positions and redshifts in actual galaxy catalogs.

  6. A study on investors’ personality characteristics and behavioral biases: Conservatism bias and availability bias in the Tehran Stock Exchange

    Directory of Open Access Journals (Sweden)

    Mahmoud Moradi

    2013-04-01

    Full Text Available Most economic and finance theories are based on the assumption that during economic decision making, people would act totally rational and consider all available information. Nevertheless, behavioral finance focuses on studying of the role of psychological factors on economic participants’ behavior. The study shows that in real-world environment, people are influenced by emotional and cognitive errors and may make irrational financial decisions. In many cases, the participants of financial markets are not aware of their talents for error in decision making, so they are dissatisfied with their investments by considering some behavioral biases decisions. These decisions may often yield undesirable outcomes, which could influence economy, significantly. This paper presents a survey on the relationship between personality dimensions with behavioral biases and availability bias among investment managers in the Tehran Stock Exchange using SPSS software, descriptive and inferential statistics. The necessary data are collected through questionnaire and they are analyzed using some statistical tests. The preliminary results indicate that there is a relationship between personality dimensions and behavioral biases like conservatism bias and availability bias among the investors in the Tehran Stock Exchange.

  7. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples

    DEFF Research Database (Denmark)

    Thorson, James T.; Kristensen, Kasper

    2016-01-01

    Statistical models play an important role in fisheries science when reconciling ecological theory with available data for wild populations or experimental studies. Ecological models increasingly include both fixed and random effects, and are often estimated using maximum likelihood techniques...... configurations of an age-structured population dynamics model. This simulation experiment shows that the epsilon-method and the existing bias-correction method perform equally well in data-rich contexts, but the epsilon-method is slightly less biased in data-poor contexts. We then apply the epsilon......-method to a spatial regression model when estimating an index of population abundance, and compare results with an alternative bias-correction algorithm that involves Markov-chain Monte Carlo sampling. This example shows that the epsilon-method leads to a biologically significant difference in estimates of average...

  8. Parameter optimization in biased decoy-state quantum key distribution with both source errors and statistical fluctuations

    Science.gov (United States)

    Zhu, Jian-Rong; Li, Jian; Zhang, Chun-Mei; Wang, Qin

    2017-10-01

    The decoy-state method has been widely used in commercial quantum key distribution (QKD) systems. In view of the practical decoy-state QKD with both source errors and statistical fluctuations, we propose a universal model of full parameter optimization in biased decoy-state QKD with phase-randomized sources. Besides, we adopt this model to carry out simulations of two widely used sources: weak coherent source (WCS) and heralded single-photon source (HSPS). Results show that full parameter optimization can significantly improve not only the secure transmission distance but also the final key generation rate. And when taking source errors and statistical fluctuations into account, the performance of decoy-state QKD using HSPS suffered less than that of decoy-state QKD using WCS.

  9. Dynamically biased statistical model for the ortho/para conversion in the H2 + H3+ → H3+ + H2 reaction

    OpenAIRE

    Gómez-Carrasco, Susana; González-Sánchez, Lola; Aguado, Alfredo; Sanz-Sanz, Cristina; Zanchet, Alexandre; Roncero, Octavio

    2012-01-01

    In this work we present a dynamically biased statistical model to describe the evolution of the title reaction from statistical to a more direct mechanism, using quasi-classical trajectories (QCT). The method is based on the one previously proposed by Park and Light [J. Chem. Phys. 126, 044305 (2007)10.1063/1.2430711]. A recent global potential energy surface is used here to calculate the capture probabilities, instead of the long-range ion-induced dipole interactions. The dynamical constrain...

  10. Biases and statistical errors in Monte Carlo burnup calculations: an unbiased stochastic scheme to solve Boltzmann/Bateman coupled equations

    International Nuclear Information System (INIS)

    Dumonteil, E.; Diop, C.M.

    2011-01-01

    External linking scripts between Monte Carlo transport codes and burnup codes, and complete integration of burnup capability into Monte Carlo transport codes, have been or are currently being developed. Monte Carlo linked burnup methodologies may serve as an excellent benchmark for new deterministic burnup codes used for advanced systems; however, there are some instances where deterministic methodologies break down (i.e., heavily angularly biased systems containing exotic materials without proper group structure) and Monte Carlo burn up may serve as an actual design tool. Therefore, researchers are also developing these capabilities in order to examine complex, three-dimensional exotic material systems that do not contain benchmark data. Providing a reference scheme implies being able to associate statistical errors to any neutronic value of interest like k(eff), reaction rates, fluxes, etc. Usually in Monte Carlo, standard deviations are associated with a particular value by performing different independent and identical simulations (also referred to as 'cycles', 'batches', or 'replicas'), but this is only valid if the calculation itself is not biased. And, as will be shown in this paper, there is a bias in the methodology that consists of coupling transport and depletion codes because Bateman equations are not linear functions of the fluxes or of the reaction rates (those quantities being always measured with an uncertainty). Therefore, we have to quantify and correct this bias. This will be achieved by deriving an unbiased minimum variance estimator of a matrix exponential function of a normal mean. The result is then used to propose a reference scheme to solve Boltzmann/Bateman coupled equations, thanks to Monte Carlo transport codes. Numerical tests will be performed with an ad hoc Monte Carlo code on a very simple depletion case and will be compared to the theoretical results obtained with the reference scheme. Finally, the statistical error propagation

  11. Dynamically biased statistical model for the ortho/para conversion in the H2+H3+ --> H3++ H2 reaction

    Science.gov (United States)

    Gómez-Carrasco, Susana; González-Sánchez, Lola; Aguado, Alfredo; Sanz-Sanz, Cristina; Zanchet, Alexandre; Roncero, Octavio

    2012-09-01

    In this work we present a dynamically biased statistical model to describe the evolution of the title reaction from statistical to a more direct mechanism, using quasi-classical trajectories (QCT). The method is based on the one previously proposed by Park and Light [J. Chem. Phys. 126, 044305 (2007), 10.1063/1.2430711]. A recent global potential energy surface is used here to calculate the capture probabilities, instead of the long-range ion-induced dipole interactions. The dynamical constraints are introduced by considering a scrambling matrix which depends on energy and determine the probability of the identity/hop/exchange mechanisms. These probabilities are calculated using QCT. It is found that the high zero-point energy of the fragments is transferred to the rest of the degrees of freedom, what shortens the lifetime of H_5^+ complexes and, as a consequence, the exchange mechanism is produced with lower proportion. The zero-point energy (ZPE) is not properly described in quasi-classical trajectory calculations and an approximation is done in which the initial ZPE of the reactants is reduced in QCT calculations to obtain a new ZPE-biased scrambling matrix. This reduction of the ZPE is explained by the need of correcting the pure classical level number of the H_5^+ complex, as done in classical simulations of unimolecular processes and to get equivalent quantum and classical rate constants using Rice-Ramsperger-Kassel-Marcus theory. This matrix allows to obtain a ratio of hop/exchange mechanisms, α(T), in rather good agreement with recent experimental results by Crabtree et al. [J. Chem. Phys. 134, 194311 (2011), 10.1063/1.3587246] at room temperature. At lower temperatures, however, the present simulations predict too high ratios because the biased scrambling matrix is not statistical enough. This demonstrates the importance of applying quantum methods to simulate this reaction at the low temperatures of astrophysical interest.

  12. The Use of a Statistical Model of Storm Surge as a Bias Correction for Dynamical Surge Models and its Applicability along the U.S. East Coast

    Directory of Open Access Journals (Sweden)

    Haydee Salmun

    2015-02-01

    Full Text Available The present study extends the applicability of a statistical model for prediction of storm surge originally developed for The Battery, NY in two ways: I. the statistical model is used as a biascorrection for operationally produced dynamical surge forecasts, and II. the statistical model is applied to the region of the east coast of the U.S. susceptible to winter extratropical storms. The statistical prediction is based on a regression relation between the “storm maximum” storm surge and the storm composite significant wave height predicted ata nearby location. The use of the statistical surge prediction as an alternative bias correction for the National Oceanic and Atmospheric Administration (NOAA operational storm surge forecasts is shownhere to be statistically equivalent to the existing bias correctiontechnique and potentially applicable for much longer forecast lead times as well as for storm surge climate prediction. Applying the statistical model to locations along the east coast shows that the regression relation can be “trained” with data from tide gauge measurements and near-shore buoys along the coast from North Carolina to Maine, and that it provides accurate estimates of storm surge.

  13. Simulating publication bias

    DEFF Research Database (Denmark)

    Paldam, Martin

    is censoring: selection by the size of estimate; SR3 selects the optimal combination of fit and size; and SR4 selects the first satisficing result. The last four SRs are steered by priors and result in bias. The MST and the FAT-PET have been developed for detection and correction of such bias. The simulations......Economic research typically runs J regressions for each selected for publication – it is often selected as the ‘best’ of the regressions. The paper examines five possible meanings of the word ‘best’: SR0 is ideal selection with no bias; SR1 is polishing: selection by statistical fit; SR2...... are made by data variation, while the model is the same. It appears that SR0 generates narrow funnels much at odds with observed funnels, while the other four funnels look more realistic. SR1 to SR4 give the mean a substantial bias that confirms the prior causing the bias. The FAT-PET MRA works well...

  14. Bias expansion of spatial statistics and approximation of differenced ...

    Indian Academy of Sciences (India)

    Our goal here is to describe and illustrate the general counting problem of inter- .... A great amount of literature exists in number theory and enumerative ..... series setting, similar consideration has been given to determine bias and mean ...

  15. Adaptable history biases in human perceptual decisions.

    Science.gov (United States)

    Abrahamyan, Arman; Silva, Laura Luz; Dakin, Steven C; Carandini, Matteo; Gardner, Justin L

    2016-06-21

    When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject's default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics.

  16. Dynamically biased statistical model for the ortho/para conversion in the H2 + H3+ → H3+ + H2 reaction.

    Science.gov (United States)

    Gómez-Carrasco, Susana; González-Sánchez, Lola; Aguado, Alfredo; Sanz-Sanz, Cristina; Zanchet, Alexandre; Roncero, Octavio

    2012-09-07

    In this work we present a dynamically biased statistical model to describe the evolution of the title reaction from statistical to a more direct mechanism, using quasi-classical trajectories (QCT). The method is based on the one previously proposed by Park and Light [J. Chem. Phys. 126, 044305 (2007)]. A recent global potential energy surface is used here to calculate the capture probabilities, instead of the long-range ion-induced dipole interactions. The dynamical constraints are introduced by considering a scrambling matrix which depends on energy and determine the probability of the identity/hop/exchange mechanisms. These probabilities are calculated using QCT. It is found that the high zero-point energy of the fragments is transferred to the rest of the degrees of freedom, what shortens the lifetime of H(5)(+) complexes and, as a consequence, the exchange mechanism is produced with lower proportion. The zero-point energy (ZPE) is not properly described in quasi-classical trajectory calculations and an approximation is done in which the initial ZPE of the reactants is reduced in QCT calculations to obtain a new ZPE-biased scrambling matrix. This reduction of the ZPE is explained by the need of correcting the pure classical level number of the H(5)(+) complex, as done in classical simulations of unimolecular processes and to get equivalent quantum and classical rate constants using Rice-Ramsperger-Kassel-Marcus theory. This matrix allows to obtain a ratio of hop/exchange mechanisms, α(T), in rather good agreement with recent experimental results by Crabtree et al. [J. Chem. Phys. 134, 194311 (2011)] at room temperature. At lower temperatures, however, the present simulations predict too high ratios because the biased scrambling matrix is not statistical enough. This demonstrates the importance of applying quantum methods to simulate this reaction at the low temperatures of astrophysical interest.

  17. Affective bias as a rational response to the statistics of rewards and punishments.

    Science.gov (United States)

    Pulcu, Erdem; Browning, Michael

    2017-10-04

    Affective bias, the tendency to differentially prioritise the processing of negative relative to positive events, is commonly observed in clinical and non-clinical populations. However, why such biases develop is not known. Using a computational framework, we investigated whether affective biases may reflect individuals' estimates of the information content of negative relative to positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of distinct positive and negative outcomes which may bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel approach to treatment development.

  18. The Probability Distribution for a Biased Spinner

    Science.gov (United States)

    Foster, Colin

    2012-01-01

    This article advocates biased spinners as an engaging context for statistics students. Calculating the probability of a biased spinner landing on a particular side makes valuable connections between probability and other areas of mathematics. (Contains 2 figures and 1 table.)

  19. Toward a Better Understanding of the Relationship between Belief in the Paranormal and Statistical Bias: The Potential Role of Schizotypy.

    Science.gov (United States)

    Dagnall, Neil; Denovan, Andrew; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

    2016-01-01

    The present paper examined relationships between schizotypy (measured by the Oxford-Liverpool Inventory of Feelings and Experience; O-LIFE scale brief), belief in the paranormal (assessed via the Revised Paranormal Belief Scale; RPBS) and proneness to statistical bias (i.e., perception of randomness and susceptibility to conjunction fallacy). Participants were 254 volunteers recruited via convenience sampling. Probabilistic reasoning problems appeared framed within both standard and paranormal contexts. Analysis revealed positive correlations between the Unusual Experience (UnExp) subscale of O-LIFE and paranormal belief measures [RPBS full scale, traditional paranormal beliefs (TPB) and new age philosophy]. Performance on standard problems correlated negatively with UnExp and belief in the paranormal (particularly the TPB dimension of the RPBS). Consideration of specific problem types revealed that perception of randomness associated more strongly with belief in the paranormal than conjunction; both problem types related similarly to UnExp. Structural equation modeling specified that belief in the paranormal mediated the indirect relationship between UnExp and statistical bias. For problems presented in a paranormal context a framing effect occurred. Whilst UnExp correlated positively with conjunction proneness (controlling for perception of randomness), there was no association between UnExp and perception of randomness (controlling for conjunction).

  20. Towards a better understanding of the relationship between belief in the paranormal and statistical bias: The potential role of schizotypy

    Directory of Open Access Journals (Sweden)

    Neil Dagnall

    2016-07-01

    Full Text Available The present paper examined relationships between schizotypy (measured by the Oxford-Liverpool Inventory of Feelings and Experience; O-LIFE scale brief, belief in the paranormal (assessed via the Revised Paranormal Belief Scale; RPBS and proneness to statistical bias (i.e., perception of randomness and susceptibility to conjunction fallacy. Participants were 254 volunteers recruited via convenience sampling. Probabilistic reasoning problems appeared framed within both standard and paranormal contexts. Analysis revealed positive correlations between the Unusual Experience subscale of O-LIFE and paranormal belief measures (RPBS full scale, traditional paranormal beliefs and new age philosophy. Performance on standard problems correlated negatively with Unusual Experience and belief in the paranormal (particularly the traditional paranormal belief dimension of the RPBS. Consideration of specific problem types revealed that perception of randomness associated more strongly with belief in the paranormal than conjunction; both problem types related similarly to Unusual Experience. Structural equation modelling specified that belief in the paranormal mediated the indirect relationship between Unusual Experience and statistical bias. For problems presented in a paranormal context a framing effect occurred. Whilst Unusual Experience correlated positively with conjunction proneness (controlling for perception of randomness, there was no association between Unusual Experience and perception of randomness (controlling for conjunction.

  1. Spatial Bias in Field-Estimated Unsaturated Hydraulic Properties

    Energy Technology Data Exchange (ETDEWEB)

    HOLT,ROBERT M.; WILSON,JOHN L.; GLASS JR.,ROBERT J.

    2000-12-21

    Hydraulic property measurements often rely on non-linear inversion models whose errors vary between samples. In non-linear physical measurement systems, bias can be directly quantified and removed using calibration standards. In hydrologic systems, field calibration is often infeasible and bias must be quantified indirectly. We use a Monte Carlo error analysis to indirectly quantify spatial bias in the saturated hydraulic conductivity, K{sub s}, and the exponential relative permeability parameter, {alpha}, estimated using a tension infiltrometer. Two types of observation error are considered, along with one inversion-model error resulting from poor contact between the instrument and the medium. Estimates of spatial statistics, including the mean, variance, and variogram-model parameters, show significant bias across a parameter space representative of poorly- to well-sorted silty sand to very coarse sand. When only observation errors are present, spatial statistics for both parameters are best estimated in materials with high hydraulic conductivity, like very coarse sand. When simple contact errors are included, the nature of the bias changes dramatically. Spatial statistics are poorly estimated, even in highly conductive materials. Conditions that permit accurate estimation of the statistics for one of the parameters prevent accurate estimation for the other; accurate regions for the two parameters do not overlap in parameter space. False cross-correlation between estimated parameters is created because estimates of K{sub s} also depend on estimates of {alpha} and both parameters are estimated from the same data.

  2. Bias correction of daily satellite precipitation data using genetic algorithm

    Science.gov (United States)

    Pratama, A. W.; Buono, A.; Hidayat, R.; Harsa, H.

    2018-05-01

    Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was producted by blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.

  3. Correction of Selection Bias in Survey Data: Is the Statistical Cure Worse Than the Bias?

    Science.gov (United States)

    Hanley, James A

    2017-04-01

    In previous articles in the American Journal of Epidemiology (Am J Epidemiol. 2013;177(5):431-442) and American Journal of Public Health (Am J Public Health. 2013;103(10):1895-1901), Masters et al. reported age-specific hazard ratios for the contrasts in mortality rates between obesity categories. They corrected the observed hazard ratios for selection bias caused by what they postulated was the nonrepresentativeness of the participants in the National Health Interview Study that increased with age, obesity, and ill health. However, it is possible that their regression approach to remove the alleged bias has not produced, and in general cannot produce, sensible hazard ratio estimates. First, we must consider how many nonparticipants there might have been in each category of obesity and of age at entry and how much higher the mortality rates would have to be in nonparticipants than in participants in these same categories. What plausible set of numerical values would convert the ("biased") decreasing-with-age hazard ratios seen in the data into the ("unbiased") increasing-with-age ratios that they computed? Can these values be encapsulated in (and can sensible values be recovered from) one additional internal variable in a regression model? Second, one must examine the age pattern of the hazard ratios that have been adjusted for selection. Without the correction, the hazard ratios are attenuated with increasing age. With it, the hazard ratios at older ages are considerably higher, but those at younger ages are well below one. Third, one must test whether the regression approach suggested by Masters et al. would correct the nonrepresentativeness that increased with age and ill health that I introduced into real and hypothetical data sets. I found that the approach did not recover the hazard ratio patterns present in the unselected data sets: the corrections overshot the target at older ages and undershot it at lower ages.

  4. Statistical Bias in Maximum Likelihood Estimators of Item Parameters.

    Science.gov (United States)

    1982-04-01

    34 a> E r’r~e r ,C Ie I# ne,..,.rVi rnd Id.,flfv b1 - bindk numb.r) I; ,t-i i-cd I ’ tiie bias in the maximum likelihood ,st i- i;, ’ t iIeiIrs in...NTC, IL 60088 Psychometric Laboratory University of North Carolina I ERIC Facility-Acquisitions Davie Hall 013A 4833 Rugby Avenue Chapel Hill, NC

  5. Empirical Comparison of Publication Bias Tests in Meta-Analysis.

    Science.gov (United States)

    Lin, Lifeng; Chu, Haitao; Murad, Mohammad Hassan; Hong, Chuan; Qu, Zhiyong; Cole, Stephen R; Chen, Yong

    2018-04-16

    Decision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data. This study compares seven commonly used publication bias tests (i.e., Begg's rank test, trim-and-fill, Egger's, Tang's, Macaskill's, Deeks', and Peters' regression tests) based on 28,655 meta-analyses available in the Cochrane Library. Egger's regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg's rank test and the trim-and-fill method. The agreement among Tang's, Macaskill's, Deeks', and Peters' regression tests for binary outcomes was moderately strong (most κ's were around 0.6). Tang's and Deeks' tests had fairly similar performance (κ > 0.9). The agreement among Begg's rank test, the trim-and-fill method, and Egger's regression test was weak or moderate (κ < 0.5). Given the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.

  6. Statistical bias correction modelling for seasonal rainfall forecast for the case of Bali island

    Science.gov (United States)

    Lealdi, D.; Nurdiati, S.; Sopaheluwakan, A.

    2018-04-01

    Rainfall is an element of climate which is highly influential to the agricultural sector. Rain pattern and distribution highly determines the sustainability of agricultural activities. Therefore, information on rainfall is very useful for agriculture sector and farmers in anticipating the possibility of extreme events which often cause failures of agricultural production. This research aims to identify the biases from seasonal forecast products from ECMWF (European Centre for Medium-Range Weather Forecasts) rainfall forecast and to build a transfer function in order to correct the distribution biases as a new prediction model using quantile mapping approach. We apply this approach to the case of Bali Island, and as a result, the use of bias correction methods in correcting systematic biases from the model gives better results. The new prediction model obtained with this approach is better than ever. We found generally that during rainy season, the bias correction approach performs better than in dry season.

  7. Systematic Biases in Parameter Estimation of Binary Black-Hole Mergers

    Science.gov (United States)

    Littenberg, Tyson B.; Baker, John G.; Buonanno, Alessandra; Kelly, Bernard J.

    2012-01-01

    Parameter estimation of binary-black-hole merger events in gravitational-wave data relies on matched filtering techniques, which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing non-spinning numerical-relativity waveforms. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios (SNR). These biases grow to be comparable to the statistical errors at high signal-to-noise ratios for ground-based instruments (SNR approximately 50) but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors but are small enough (at most a few percent in the black-hole masses) that we expect they should not affect broad astrophysical conclusions that may be drawn from the data.

  8. Modular reweighting software for statistical mechanical analysis of biased equilibrium data

    Science.gov (United States)

    Sindhikara, Daniel J.

    2012-07-01

    Here a simple, useful, modular approach and software suite designed for statistical reweighting and analysis of equilibrium ensembles is presented. Statistical reweighting is useful and sometimes necessary for analysis of equilibrium enhanced sampling methods, such as umbrella sampling or replica exchange, and also in experimental cases where biasing factors are explicitly known. Essentially, statistical reweighting allows extrapolation of data from one or more equilibrium ensembles to another. Here, the fundamental separable steps of statistical reweighting are broken up into modules - allowing for application to the general case and avoiding the black-box nature of some “all-inclusive” reweighting programs. Additionally, the programs included are, by-design, written with little dependencies. The compilers required are either pre-installed on most systems, or freely available for download with minimal trouble. Examples of the use of this suite applied to umbrella sampling and replica exchange molecular dynamics simulations will be shown along with advice on how to apply it in the general case. New version program summaryProgram title: Modular reweighting version 2 Catalogue identifier: AEJH_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJH_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 179 118 No. of bytes in distributed program, including test data, etc.: 8 518 178 Distribution format: tar.gz Programming language: C++, Python 2.6+, Perl 5+ Computer: Any Operating system: Any RAM: 50-500 MB Supplementary material: An updated version of the original manuscript (Comput. Phys. Commun. 182 (2011) 2227) is available Classification: 4.13 Catalogue identifier of previous version: AEJH_v1_0 Journal reference of previous version: Comput. Phys. Commun. 182 (2011) 2227 Does the new

  9. Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias.

    Directory of Open Access Journals (Sweden)

    Thomas Pfeiffer

    2011-03-01

    Full Text Available Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63% relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%. Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%. Such dynamic patterns in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust

  10. Galaxy bias and primordial non-Gaussianity

    Energy Technology Data Exchange (ETDEWEB)

    Assassi, Valentin; Baumann, Daniel [DAMTP, Cambridge University, Wilberforce Road, Cambridge CB3 0WA (United Kingdom); Schmidt, Fabian, E-mail: assassi@ias.edu, E-mail: D.D.Baumann@uva.nl, E-mail: fabians@MPA-Garching.MPG.DE [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching (Germany)

    2015-12-01

    We present a systematic study of galaxy biasing in the presence of primordial non-Gaussianity. For a large class of non-Gaussian initial conditions, we define a general bias expansion and prove that it is closed under renormalization, thereby showing that the basis of operators in the expansion is complete. We then study the effects of primordial non-Gaussianity on the statistics of galaxies. We show that the equivalence principle enforces a relation between the scale-dependent bias in the galaxy power spectrum and that in the dipolar part of the bispectrum. This provides a powerful consistency check to confirm the primordial origin of any observed scale-dependent bias. Finally, we also discuss the imprints of anisotropic non-Gaussianity as motivated by recent studies of higher-spin fields during inflation.

  11. Galaxy bias and primordial non-Gaussianity

    International Nuclear Information System (INIS)

    Assassi, Valentin; Baumann, Daniel; Schmidt, Fabian

    2015-01-01

    We present a systematic study of galaxy biasing in the presence of primordial non-Gaussianity. For a large class of non-Gaussian initial conditions, we define a general bias expansion and prove that it is closed under renormalization, thereby showing that the basis of operators in the expansion is complete. We then study the effects of primordial non-Gaussianity on the statistics of galaxies. We show that the equivalence principle enforces a relation between the scale-dependent bias in the galaxy power spectrum and that in the dipolar part of the bispectrum. This provides a powerful consistency check to confirm the primordial origin of any observed scale-dependent bias. Finally, we also discuss the imprints of anisotropic non-Gaussianity as motivated by recent studies of higher-spin fields during inflation

  12. Common method biases in behavioral research: a critical review of the literature and recommended remedies.

    Science.gov (United States)

    Podsakoff, Philip M; MacKenzie, Scott B; Lee, Jeong-Yeon; Podsakoff, Nathan P

    2003-10-01

    Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

  13. Presence of bias in radiographer plain film reading performance studies

    International Nuclear Information System (INIS)

    Brealey, S.; Scally, A.J.; Thomas, N.B.

    2002-01-01

    Purpose To raise awareness of the frequency of bias that can affect the quality of radiographer plain film reading performance studies. Methods Studies that assessed radiographer(s) plain film reading performance were located by searching electronic databases and grey literature, hand-searching journals, personal communication and scanning reference lists. Thirty studies were judged eligible from all data sources. Results A one-way analysis of variance (ANOVA) demonstrates no statistically significant difference (P=0.25) in the mean proportion of biases present from diagnostic accuracy (0.37), performance (0.42) and outcome (0.44) study designs. Pearson's correlation coefficient showed no statistically significant linear association between the proportion of biases present for the three different study designs and the year that the study was performed. The frequency of biases in film and observer selection and application of the reference standard was quite low. In contrast, many biases were present concerning independence of film reporting and comparison of reports for concordance. Conclusions The findings indicate variation in the presence of bias in radiographer plain film reading performance studies. The careful consideration of bias is an essential component of study quality and hence the validity of the evidence-base used to underpin radiographic reporting policy

  14. Structure formation from non-Gaussian initial conditions: Multivariate biasing, statistics, and comparison with N-body simulations

    International Nuclear Information System (INIS)

    Giannantonio, Tommaso; Porciani, Cristiano

    2010-01-01

    We study structure formation in the presence of primordial non-Gaussianity of the local type with parameters f NL and g NL . We show that the distribution of dark-matter halos is naturally described by a multivariate bias scheme where the halo overdensity depends not only on the underlying matter density fluctuation δ but also on the Gaussian part of the primordial gravitational potential φ. This corresponds to a non-local bias scheme in terms of δ only. We derive the coefficients of the bias expansion as a function of the halo mass by applying the peak-background split to common parametrizations for the halo mass function in the non-Gaussian scenario. We then compute the halo power spectrum and halo-matter cross spectrum in the framework of Eulerian perturbation theory up to third order. Comparing our results against N-body simulations, we find that our model accurately describes the numerical data for wave numbers k≤0.1-0.3h Mpc -1 depending on redshift and halo mass. In our multivariate approach, perturbations in the halo counts trace φ on large scales, and this explains why the halo and matter power spectra show different asymptotic trends for k→0. This strongly scale-dependent bias originates from terms at leading order in our expansion. This is different from what happens using the standard univariate local bias where the scale-dependent terms come from badly behaved higher-order corrections. On the other hand, our biasing scheme reduces to the usual local bias on smaller scales, where |φ| is typically much smaller than the density perturbations. We finally discuss the halo bispectrum in the context of multivariate biasing and show that, due to its strong scale and shape dependence, it is a powerful tool for the detection of primordial non-Gaussianity from future galaxy surveys.

  15. A comparator-hypothesis account of biased contingency detection.

    Science.gov (United States)

    Vadillo, Miguel A; Barberia, Itxaso

    2018-02-12

    Our ability to detect statistical dependencies between different events in the environment is strongly biased by the number of coincidences between them. Even when there is no true covariation between a cue and an outcome, if the marginal probability of either of them is high, people tend to perceive some degree of statistical contingency between both events. The present paper explores the ability of the Comparator Hypothesis to explain the general pattern of results observed in this literature. Our simulations show that this model can account for the biasing effects of the marginal probabilities of cues and outcomes. Furthermore, the overall fit of the Comparator Hypothesis to a sample of experimental conditions from previous studies is comparable to that of the popular Rescorla-Wagner model. These results should encourage researchers to further explore and put to the test the predictions of the Comparator Hypothesis in the domain of biased contingency detection. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. Correction of stream quality trends for the effects of laboratory measurement bias

    Science.gov (United States)

    Alexander, Richard B.; Smith, Richard A.; Schwarz, Gregory E.

    1993-01-01

    We present a statistical model relating measurements of water quality to associated errors in laboratory methods. Estimation of the model allows us to correct trends in water quality for long-term and short-term variations in laboratory measurement errors. An illustration of the bias correction method for a large national set of stream water quality and quality assurance data shows that reductions in the bias of estimates of water quality trend slopes are achieved at the expense of increases in the variance of these estimates. Slight improvements occur in the precision of estimates of trend in bias by using correlative information on bias and water quality to estimate random variations in measurement bias. The results of this investigation stress the need for reliable, long-term quality assurance data and efficient statistical methods to assess the effects of measurement errors on the detection of water quality trends.

  17. Bias against research on gender bias.

    Science.gov (United States)

    Cislak, Aleksandra; Formanowicz, Magdalena; Saguy, Tamar

    2018-01-01

    The bias against women in academia is a documented phenomenon that has had detrimental consequences, not only for women, but also for the quality of science. First, gender bias in academia affects female scientists, resulting in their underrepresentation in academic institutions, particularly in higher ranks. The second type of gender bias in science relates to some findings applying only to male participants, which produces biased knowledge. Here, we identify a third potentially powerful source of gender bias in academia: the bias against research on gender bias. In a bibliometric investigation covering a broad range of social sciences, we analyzed published articles on gender bias and race bias and established that articles on gender bias are funded less often and published in journals with a lower Impact Factor than articles on comparable instances of social discrimination. This result suggests the possibility of an underappreciation of the phenomenon of gender bias and related research within the academic community. Addressing this meta-bias is crucial for the further examination of gender inequality, which severely affects many women across the world.

  18. Bias and Causation Models and Judgment for Valid Comparisons

    CERN Document Server

    Weisberg, Herbert I

    2010-01-01

    A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects. Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation pre

  19. Bias identification in PWR pressurizer instrumentation using the generalized liklihood-ratio technique

    International Nuclear Information System (INIS)

    Tylee, J.L.

    1981-01-01

    A method for detecting and identifying biases in the pressure and level sensors of a pressurized water reactor (PWR) pressurizer is described. The generalized likelihood ratio (GLR) technique performs statistical tests on the innovations sequence of a Kalman filter state estimator and is capable of determining when a bias appears, in what sensor the bias exists, and estimating the bias magnitude. Simulation results using a second-order linear, discrete PWR pressurizer model demonstrate the capabilities of the GLR method

  20. Effects of Model Chemistry and Data Biases on Stratospheric Ozone Assimilation

    National Research Council Canada - National Science Library

    Coy, L; Allen, D. R; Eckermann, S. D; McCormack, J. P; Stajner, I; Hogan, T. F

    2007-01-01

    .... In this study, O-F statistics from the Global Ozone Assimilation Testing System (GOATS) are used to examine how ozone assimilation products and their associated O-F statistics depend on input data biases and ozone photochemistry parameterizations (OPP...

  1. Statistical electromagnetics: Complex cavities

    NARCIS (Netherlands)

    Naus, H.W.L.

    2008-01-01

    A selection of the literature on the statistical description of electromagnetic fields and complex cavities is concisely reviewed. Some essential concepts, for example, the application of the central limit theorem and the maximum entropy principle, are scrutinized. Implicit assumptions, biased

  2. Sampling of temporal networks: Methods and biases

    Science.gov (United States)

    Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter

    2017-11-01

    Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

  3. Domain-size-dependent exchange bias in Co/LaFeO3

    Energy Technology Data Exchange (ETDEWEB)

    Scholl, A.; Nolting, F.; Seo, J.W.; Ohldag, H.; Stohr, J.; Raoux,S.; Locquet, J.-P.; Fompeyrine, J.

    2004-09-22

    X-ray microscopy using magnetic linear dichroism of a zero-field-grown, multi-domain Co/LaFeO{sub 3} ferromagnet/antiferromagnet sample shows a local exchange bias of random direction and magnitude. A statistical analysis of the local bias of individual, micron-size magnetic domains demonstrates an increasing bias field with decreasing domain size as expected for a random distribution of pinned, uncompensated spins, which are believed to mediate the interface coupling. A linear dependence with the inverse domain diameter is found.

  4. The Use of Official Statistics in Self-Selection Bias Modeling

    Directory of Open Access Journals (Sweden)

    Dalla Valle Luciana

    2016-12-01

    Full Text Available Official statistics are a fundamental source of publicly available information that periodically provides a great amount of data on all major areas of citizens’ lives, such as economics, social development, education, and the environment. However, these extraordinary sources of information are often neglected, especially by business and industrial statisticians. In particular, data collected from small businesses, like small and medium-sized enterprizes (SMEs, are rarely integrated with official statistics data.

  5. TCABR Tokamak scrape-off layer turbulence with DC biasing

    International Nuclear Information System (INIS)

    Heller, M.V.A.P.; Ferreira, A.A.; Caldas, I.L.; Nascimento, I.C.

    2004-01-01

    Turbulence and particle transport in plasma scrape-off layer have been controlled by external electric fields. This control can be achieved by a biasing electrode located inside the plasma. We investigate plasma turbulence changes in the scrape-off layer of TCABR tokamak introduced by DC biasing an electrode inside the plasma. Our investigation is based on the alterations observed on the wavelet power spectra and on the intermittent burst sequences of plasma potential and density fluctuations measured by a set of Langmuir probes. Biasing the electrode changes the turbulence statistics and the bursts intermittence. With the imposed external electric field, fluctuation amplitudes, phase velocities, and anomalous particle transport are modified. Transport reduction for higher frequencies induced by the biasing could be due to the strong de-phasing between density and potential fluctuations. The mode coupling increases with the perturbation for the high frequency broadband fluctuations. The total (laminar and bursting) radial particle transport is reduced by about 25% by DC biasing. Bursts contribution to total transport is 15% and for the studied conditions this contribution does not change much with the bias perturbation

  6. An experimental verification of laser-velocimeter sampling bias and its correction

    Science.gov (United States)

    Johnson, D. A.; Modarress, D.; Owen, F. K.

    1982-01-01

    The existence of 'sampling bias' in individual-realization laser velocimeter measurements is experimentally verified and shown to be independent of sample rate. The experiments were performed in a simple two-stream mixing shear flow with the standard for comparison being laser-velocimeter results obtained under continuous-wave conditions. It is also demonstrated that the errors resulting from sampling bias can be removed by a proper interpretation of the sampling statistics. In addition, data obtained in a shock-induced separated flow and in the near-wake of airfoils are presented, both bias-corrected and uncorrected, to illustrate the effects of sampling bias in the extreme.

  7. A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield.

    Science.gov (United States)

    Ringard, Justine; Seyler, Frederique; Linguet, Laurent

    2017-06-16

    Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.

  8. Ensemble stacking mitigates biases in inference of synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Brendan Chambers

    2018-03-01

    Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

  9. Publication Bias in Meta-Analyses of the Efficacy of Psychotherapeutic Interventions for Depression

    Science.gov (United States)

    Niemeyer, Helen; Musch, Jochen; Pietrowsky, Reinhard

    2013-01-01

    Objective: The aim of this study was to assess whether systematic reviews investigating psychotherapeutic interventions for depression are affected by publication bias. Only homogeneous data sets were included, as heterogeneous data sets can distort statistical tests of publication bias. Method: We applied Begg and Mazumdar's adjusted rank…

  10. Operator Bias in the Estimation of Arc Efficiency in Gas Tungsten Arc Welding

    Directory of Open Access Journals (Sweden)

    Fredrik Sikström

    2015-03-01

    Full Text Available In this paper the operator bias in the measurement process of arc efficiency in stationary direct current electrode negative gas tungsten arc welding is discussed. An experimental study involving 15 operators (enough to reach statistical significance has been carried out with the purpose to estimate the arc efficiency from a specific procedure for calorimetric experiments. The measurement procedure consists of three manual operations which introduces operator bias in the measurement process. An additional relevant experiment highlights the consequences of estimating the arc voltage by measuring the potential between the terminals of the welding power source instead of measuring the potential between the electrode contact tube and the workpiece. The result of the study is a statistical evaluation of the operator bias influence on the estimate, showing that operator bias is negligible in the estimate considered here. On the contrary the consequences of neglecting welding leads voltage drop results in a significant under estimation of the arc efficiency.

  11. Assessing the extent of non-stationary biases in GCMs

    Science.gov (United States)

    Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

    2017-06-01

    General circulation models (GCMs) are the main tools for estimating changes in the climate for the future. The imperfect representation of climate models introduces biases in the simulations that need to be corrected prior to their use for impact assessments. Bias correction methods generally assume that the bias calculated over the historical period does not change and can be applied to the future. This study investigates this assumption by considering the extent and nature of bias non-stationarity using 20th century precipitation and temperature simulations from six CMIP5 GCMs across Australia. Four statistics (mean, standard deviation, 10th and 90th quantiles) in monthly and seasonal biases are obtained for three different time window lengths (10, 25 and 33 years) to examine the properties of bias over time. This approach is repeated for two different phases of the Interdecadal Pacific Oscillation (IPO), which is known to have strong influences on the Australian climate. It is found that bias non-stationarity at decadal timescales is indeed an issue over some of Australia for some GCMs. When considering interdecadal variability there are significant difference in the bias between positive and negative phases of the IPO. Regional analyses confirmed these findings with the largest differences seen on the east coast of Australia, where IPO impacts tend to be the strongest. The nature of the bias non-stationarity found in this study suggests that it will be difficult to modify existing bias correction approaches to account for non-stationary biases. A more practical approach for impact assessments that use bias correction maybe to use a selection of GCMs where the assumption of bias non-stationarity holds.

  12. Automated statistical modeling of analytical measurement systems

    International Nuclear Information System (INIS)

    Jacobson, J.J.

    1992-01-01

    The statistical modeling of analytical measurement systems at the Idaho Chemical Processing Plant (ICPP) has been completely automated through computer software. The statistical modeling of analytical measurement systems is one part of a complete quality control program used by the Remote Analytical Laboratory (RAL) at the ICPP. The quality control program is an integration of automated data input, measurement system calibration, database management, and statistical process control. The quality control program and statistical modeling program meet the guidelines set forth by the American Society for Testing Materials and American National Standards Institute. A statistical model is a set of mathematical equations describing any systematic bias inherent in a measurement system and the precision of a measurement system. A statistical model is developed from data generated from the analysis of control standards. Control standards are samples which are made up at precise known levels by an independent laboratory and submitted to the RAL. The RAL analysts who process control standards do not know the values of those control standards. The object behind statistical modeling is to describe real process samples in terms of their bias and precision and, to verify that a measurement system is operating satisfactorily. The processing of control standards gives us this ability

  13. Statistical implications in Monte Carlo depletions - 051

    International Nuclear Information System (INIS)

    Zhiwen, Xu; Rhodes, J.; Smith, K.

    2010-01-01

    As a result of steady advances of computer power, continuous-energy Monte Carlo depletion analysis is attracting considerable attention for reactor burnup calculations. The typical Monte Carlo analysis is set up as a combination of a Monte Carlo neutron transport solver and a fuel burnup solver. Note that the burnup solver is a deterministic module. The statistical errors in Monte Carlo solutions are introduced into nuclide number densities and propagated along fuel burnup. This paper is towards the understanding of the statistical implications in Monte Carlo depletions, including both statistical bias and statistical variations in depleted fuel number densities. The deterministic Studsvik lattice physics code, CASMO-5, is modified to model the Monte Carlo depletion. The statistical bias in depleted number densities is found to be negligible compared to its statistical variations, which, in turn, demonstrates the correctness of the Monte Carlo depletion method. Meanwhile, the statistical variation in number densities generally increases with burnup. Several possible ways of reducing the statistical errors are discussed: 1) to increase the number of individual Monte Carlo histories; 2) to increase the number of time steps; 3) to run additional independent Monte Carlo depletion cases. Finally, a new Monte Carlo depletion methodology, called the batch depletion method, is proposed, which consists of performing a set of independent Monte Carlo depletions and is thus capable of estimating the overall statistical errors including both the local statistical error and the propagated statistical error. (authors)

  14. Testing human sperm chemotaxis: how to detect biased motion in population assays.

    Directory of Open Access Journals (Sweden)

    Leah Armon

    Full Text Available Biased motion of motile cells in a concentration gradient of a chemoattractant is frequently studied on the population level. This approach has been particularly employed in human sperm chemotactic assays, where the fraction of responsive cells is low and detection of biased motion depends on subtle differences. In these assays, statistical measures such as population odds ratios of swimming directions can be employed to infer chemotactic performance. Here, we report on an improved method to assess statistical significance of experimentally determined odds ratios and discuss the strong impact of data correlations that arise from the directional persistence of sperm swimming.

  15. Length bias correction in gene ontology enrichment analysis using logistic regression.

    Science.gov (United States)

    Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

    2012-01-01

    When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

  16. An introduction to Bartlett correction and bias reduction

    CERN Document Server

    Cordeiro, Gauss M

    2014-01-01

    This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique, and discuss concrete applications to several important statistical models.

  17. Constructing diagnostic likelihood: clinical decisions using subjective versus statistical probability.

    Science.gov (United States)

    Kinnear, John; Jackson, Ruth

    2017-07-01

    Although physicians are highly trained in the application of evidence-based medicine, and are assumed to make rational decisions, there is evidence that their decision making is prone to biases. One of the biases that has been shown to affect accuracy of judgements is that of representativeness and base-rate neglect, where the saliency of a person's features leads to overestimation of their likelihood of belonging to a group. This results in the substitution of 'subjective' probability for statistical probability. This study examines clinicians' propensity to make estimations of subjective probability when presented with clinical information that is considered typical of a medical condition. The strength of the representativeness bias is tested by presenting choices in textual and graphic form. Understanding of statistical probability is also tested by omitting all clinical information. For the questions that included clinical information, 46.7% and 45.5% of clinicians made judgements of statistical probability, respectively. Where the question omitted clinical information, 79.9% of clinicians made a judgement consistent with statistical probability. There was a statistically significant difference in responses to the questions with and without representativeness information (χ2 (1, n=254)=54.45, pprobability. One of the causes for this representativeness bias may be the way clinical medicine is taught where stereotypic presentations are emphasised in diagnostic decision making. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  18. Researchers' Perceptions of Statistical Significance Contribute to Bias in Health and Exercise Science

    Science.gov (United States)

    Buchanan, Taylor L.; Lohse, Keith R.

    2016-01-01

    We surveyed researchers in the health and exercise sciences to explore different areas and magnitudes of bias in researchers' decision making. Participants were presented with scenarios (testing a central hypothesis with p = 0.06 or p = 0.04) in a random order and surveyed about what they would do in each scenario. Participants showed significant…

  19. Biased sampling, over-identified parameter problems and beyond

    CERN Document Server

    Qin, Jing

    2017-01-01

    This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. .

  20. Statistical analysis of angular correlation measurements

    International Nuclear Information System (INIS)

    Oliveira, R.A.A.M. de.

    1986-01-01

    Obtaining the multipole mixing ratio, δ, of γ transitions in angular correlation measurements is a statistical problem characterized by the small number of angles in which the observation is made and by the limited statistic of counting, α. The inexistence of a sufficient statistics for the estimator of δ, is shown. Three different estimators for δ were constructed and their properties of consistency, bias and efficiency were tested. Tests were also performed in experimental results obtained in γ-γ directional correlation measurements. (Author) [pt

  1. The specific bias in dynamic Monte Carlo simulations of nuclear reactors

    International Nuclear Information System (INIS)

    Yamamoto, T.; Endo, H.; Ishizu, T.; Tatewaki, I.

    2013-01-01

    During the development of Monte-Carlo-based dynamic code system, we have encountered two major Monte-Carlo-specific problems. One is the break down due to 'false super-criticality' which is caused by an accidentally large eigenvalue due to statistical error in spite of the fact that the reactor is actually not critical. The other problem, which is the main topic in this paper, is that the statistical error in power level using the reactivity calculated with Monte Carlo code is not symmetric about its mean but always positively biased. This signifies that the bias is accumulated as the calculation proceeds and consequently results in an over-estimation of the final power level. It should be noted that the bias will not be eliminated by refining the time step as long as the variance is not zero. A preliminary investigation on this matter using the one-group-precursor point kinetic equations was made and it was concluded that the bias in power level is approximately proportional to the product of variance in Monte Carlo calculation and elapsed time. This conclusion was verified with some numerical experiments. This outcome is important in quantifying the required precision of the Monte-Carlo-based reactivity calculations. (authors)

  2. Bias in regression coefficient estimates upon different treatments of ...

    African Journals Online (AJOL)

    MS and PW consistently overestimated the population parameter. EM and RI, on the other hand, tended to consistently underestimate the population parameter under non-monotonic pattern. Keywords: Missing data, bias, regression, percent missing, non-normality, missing pattern > East African Journal of Statistics Vol.

  3. Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association

    Science.gov (United States)

    Grinde, Kelsey E.; Arbet, Jaron; Green, Alden; O'Connell, Michael; Valcarcel, Alessandra; Westra, Jason; Tintle, Nathan

    2017-01-01

    To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures. PMID:28959274

  4. Sources of method bias in social science research and recommendations on how to control it.

    Science.gov (United States)

    Podsakoff, Philip M; MacKenzie, Scott B; Podsakoff, Nathan P

    2012-01-01

    Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

  5. Ensemble stacking mitigates biases in inference of synaptic connectivity.

    Science.gov (United States)

    Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

    2018-01-01

    A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

  6. A MORET tool to assist code bias estimation

    International Nuclear Information System (INIS)

    Fernex, F.; Richet, Y.; Letang, E.

    2003-01-01

    This new Graphical User Interface (GUI) developed in JAVA is one of the post-processing tools for MORET4 code. It aims to help users to estimate the importance of the k eff bias due to the code in order to better define the upper safety limit. Moreover, it allows visualizing the distance between an actual configuration case and evaluated critical experiments. This tool depends on a validated experiments database, on sets of physical parameters and on various statistical tools allowing interpolating the calculation bias of the database or displaying the projections of experiments on a reduced base of parameters. The development of this tool is still in progress. (author)

  7. Bias correction for rainrate retrievals from satellite passive microwave sensors

    Science.gov (United States)

    Short, David A.

    1990-01-01

    Rainrates retrieved from past and present satellite-borne microwave sensors are affected by a fundamental remote sensing problem. Sensor fields-of-view are typically large enough to encompass substantial rainrate variability, whereas the retrieval algorithms, based on radiative transfer calculations, show a non-linear relationship between rainrate and microwave brightness temperature. Retrieved rainrates are systematically too low. A statistical model of the bias problem shows that bias correction factors depend on the probability distribution of instantaneous rainrate and on the average thickness of the rain layer.

  8. Philosophy of statistics

    CERN Document Server

    Forster, Malcolm R

    2011-01-01

    Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling "restricted” by their disciplines or thinking "piecemeal” in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers' favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines.

  9. Is there gender bias in nursing research?

    Science.gov (United States)

    Polit, Denise F; Beck, Cheryl Tatano

    2008-10-01

    Using data from a consecutive sample of 259 studies published in four leading nursing research journals in 2005-2006, we examined whether nurse researchers favor females as study participants. On average, 75.3% of study participants were female, and 38% of studies had all-female samples. The bias favoring female participants was statistically significant and persistent. The bias was observed regardless of funding source, methodological features, and other participant and researcher characteristics, with one exception: studies that had male investigators had more sex-balanced samples. When designing studies, nurse researchers need to pay close attention to who will benefit from their research and to whether they are leaving out a specific group about which there is a gap in knowledge. (c) 2008 Wiley Periodicals, Inc.

  10. The Effects and Side-Effects of Statistics Education: Psychology Students' (Mis-)Conceptions of Probability

    Science.gov (United States)

    Morsanyi, Kinga; Primi, Caterina; Chiesi, Francesca; Handley, Simon

    2009-01-01

    In three studies we looked at two typical misconceptions of probability: the representativeness heuristic, and the equiprobability bias. The literature on statistics education predicts that some typical errors and biases (e.g., the equiprobability bias) increase with education, whereas others decrease. This is in contrast with reasoning theorists'…

  11. Codon Bias Patterns of E. coli's Interacting Proteins.

    Directory of Open Access Journals (Sweden)

    Maddalena Dilucca

    Full Text Available Synonymous codons, i.e., DNA nucleotide triplets coding for the same amino acid, are used differently across the variety of living organisms. The biological meaning of this phenomenon, known as codon usage bias, is still controversial. In order to shed light on this point, we propose a new codon bias index, CompAI, that is based on the competition between cognate and near-cognate tRNAs during translation, without being tuned to the usage bias of highly expressed genes. We perform a genome-wide evaluation of codon bias for E.coli, comparing CompAI with other widely used indices: tAI, CAI, and Nc. We show that CompAI and tAI capture similar information by being positively correlated with gene conservation, measured by the Evolutionary Retention Index (ERI, and essentiality, whereas, CAI and Nc appear to be less sensitive to evolutionary-functional parameters. Notably, the rate of variation of tAI and CompAI with ERI allows to obtain sets of genes that consistently belong to specific clusters of orthologous genes (COGs. We also investigate the correlation of codon bias at the genomic level with the network features of protein-protein interactions in E.coli. We find that the most densely connected communities of the network share a similar level of codon bias (as measured by CompAI and tAI. Conversely, a small difference in codon bias between two genes is, statistically, a prerequisite for the corresponding proteins to interact. Importantly, among all codon bias indices, CompAI turns out to have the most coherent distribution over the communities of the interactome, pointing to the significance of competition among cognate and near-cognate tRNAs for explaining codon usage adaptation. Notably, CompAI may potentially correlate with translation speed measurements, by accounting for the specific delay induced by wobble-pairing between codons and anticodons.

  12. The disagreeable behaviour of the kappa statistic.

    Science.gov (United States)

    Flight, Laura; Julious, Steven A

    2015-01-01

    It is often of interest to measure the agreement between a number of raters when an outcome is nominal or ordinal. The kappa statistic is used as a measure of agreement. The statistic is highly sensitive to the distribution of the marginal totals and can produce unreliable results. Other statistics such as the proportion of concordance, maximum attainable kappa and prevalence and bias adjusted kappa should be considered to indicate how well the kappa statistic represents agreement in the data. Each kappa should be considered and interpreted based on the context of the data being analysed. Copyright © 2014 John Wiley & Sons, Ltd.

  13. Publication bias in dermatology systematic reviews and meta-analyses.

    Science.gov (United States)

    Atakpo, Paul; Vassar, Matt

    2016-05-01

    Systematic reviews and meta-analyses in dermatology provide high-level evidence for clinicians and policy makers that influence clinical decision making and treatment guidelines. One methodological problem with systematic reviews is the under representation of unpublished studies. This problem is due in part to publication bias. Omission of statistically non-significant data from meta-analyses may result in overestimation of treatment effect sizes which may lead to clinical consequences. Our goal was to assess whether systematic reviewers in dermatology evaluate and report publication bias. Further, we wanted to conduct our own evaluation of publication bias on meta-analyses that failed to do so. Our study considered systematic reviews and meta-analyses from ten dermatology journals from 2006 to 2016. A PubMed search was conducted, and all full-text articles that met our inclusion criteria were retrieved and coded by the primary author. 293 articles were included in our analysis. Additionally, we formally evaluated publication bias in meta-analyses that failed to do so using trim and fill and cumulative meta-analysis by precision methods. Publication bias was mentioned in 107 articles (36.5%) and was formally evaluated in 64 articles (21.8%). Visual inspection of a funnel plot was the most common method of evaluating publication bias. Publication bias was present in 45 articles (15.3%), not present in 57 articles (19.5%) and not determined in 191 articles (65.2%). Using the trim and fill method, 7 meta-analyses (33.33%) showed evidence of publication bias. Although the trim and fill method only found evidence of publication bias in 7 meta-analyses, the cumulative meta-analysis by precision method found evidence of publication bias in 15 meta-analyses (71.4%). Many of the reviews in our study did not mention or evaluate publication bias. Further, of the 42 articles that stated following PRISMA reporting guidelines, 19 (45.2%) evaluated for publication bias. In

  14. Variance bias analysis for the Gelbard's batch method

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Jae Uk; Shim, Hyung Jin [Seoul National Univ., Seoul (Korea, Republic of)

    2014-05-15

    In this paper, variances and the bias will be derived analytically when the Gelbard's batch method is applied. And then, the real variance estimated from this bias will be compared with the real variance calculated from replicas. Variance and the bias were derived analytically when the batch method was applied. If the batch method was applied to calculate the sample variance, covariance terms between tallies which exist in the batch were eliminated from the bias. With the 2 by 2 fission matrix problem, we could calculate real variance regardless of whether or not the batch method was applied. However as batch size got larger, standard deviation of real variance was increased. When we perform a Monte Carlo estimation, we could get a sample variance as the statistical uncertainty of it. However, this value is smaller than the real variance of it because a sample variance is biased. To reduce this bias, Gelbard devised the method which is called the Gelbard's batch method. It has been certificated that a sample variance get closer to the real variance when the batch method is applied. In other words, the bias get reduced. This fact is well known to everyone in the MC field. However, so far, no one has given the analytical interpretation on it.

  15. Obesity, the endocannabinoid system, and bias arising from pharmaceutical sponsorship.

    Directory of Open Access Journals (Sweden)

    John M McPartland

    Full Text Available Previous research has shown that academic physicians conflicted by funding from the pharmaceutical industry have corrupted evidence based medicine and helped enlarge the market for drugs. Physicians made pharmaceutical-friendly statements, engaged in disease mongering, and signed biased review articles ghost-authored by corporate employees. This paper tested the hypothesis that bias affects review articles regarding rimonabant, an anti-obesity drug that blocks the central cannabinoid receptor.A MEDLINE search was performed for rimonabant review articles, limited to articles authored by USA physicians who served as consultants for the company that manufactures rimonabant. Extracted articles were examined for industry-friendly bias, identified by three methods: analysis with a validated instrument for monitoring bias in continuing medical education (CME; analysis for bias defined as statements that ran contrary to external evidence; and a tally of misrepresentations about the endocannabinoid system. Eight review articles were identified, but only three disclosed authors' financial conflicts of interest, despite easily accessible information to the contrary. The Takhar CME bias instrument demonstrated statistically significant bias in all the review articles. Biased statements that were nearly identical reappeared in the articles, including disease mongering, exaggerating rimonabant's efficacy and safety, lack of criticisms regarding rimonabant clinical trials, and speculations about surrogate markers stated as facts. Distinctive and identical misrepresentations regarding the endocannabinoid system also reappeared in articles by different authors.The findings are characteristic of bias that arises from financial conflicts of interest, and suggestive of ghostwriting by a common author. Resolutions for this scenario are proposed.

  16. Obesity, the endocannabinoid system, and bias arising from pharmaceutical sponsorship.

    Science.gov (United States)

    McPartland, John M

    2009-01-01

    Previous research has shown that academic physicians conflicted by funding from the pharmaceutical industry have corrupted evidence based medicine and helped enlarge the market for drugs. Physicians made pharmaceutical-friendly statements, engaged in disease mongering, and signed biased review articles ghost-authored by corporate employees. This paper tested the hypothesis that bias affects review articles regarding rimonabant, an anti-obesity drug that blocks the central cannabinoid receptor. A MEDLINE search was performed for rimonabant review articles, limited to articles authored by USA physicians who served as consultants for the company that manufactures rimonabant. Extracted articles were examined for industry-friendly bias, identified by three methods: analysis with a validated instrument for monitoring bias in continuing medical education (CME); analysis for bias defined as statements that ran contrary to external evidence; and a tally of misrepresentations about the endocannabinoid system. Eight review articles were identified, but only three disclosed authors' financial conflicts of interest, despite easily accessible information to the contrary. The Takhar CME bias instrument demonstrated statistically significant bias in all the review articles. Biased statements that were nearly identical reappeared in the articles, including disease mongering, exaggerating rimonabant's efficacy and safety, lack of criticisms regarding rimonabant clinical trials, and speculations about surrogate markers stated as facts. Distinctive and identical misrepresentations regarding the endocannabinoid system also reappeared in articles by different authors. The findings are characteristic of bias that arises from financial conflicts of interest, and suggestive of ghostwriting by a common author. Resolutions for this scenario are proposed.

  17. Bias-correction and Spatial Disaggregation for Climate Change Impact Assessments at a basin scale

    Science.gov (United States)

    Nyunt, Cho; Koike, Toshio; Yamamoto, Akio; Nemoto, Toshihoro; Kitsuregawa, Masaru

    2013-04-01

    Basin-scale climate change impact studies mainly rely on general circulation models (GCMs) comprising the related emission scenarios. Realistic and reliable data from GCM is crucial for national scale or basin scale impact and vulnerability assessments to build safety society under climate change. However, GCM fail to simulate regional climate features due to the imprecise parameterization schemes in atmospheric physics and coarse resolution scale. This study describes how to exclude some unsatisfactory GCMs with respect to focused basin, how to minimize the biases of GCM precipitation through statistical bias correction and how to cover spatial disaggregation scheme, a kind of downscaling, within in a basin. GCMs rejection is based on the regional climate features of seasonal evolution as a bench mark and mainly depends on spatial correlation and root mean square error of precipitation and atmospheric variables over the target region. Global Precipitation Climatology Project (GPCP) and Japanese 25-uear Reanalysis Project (JRA-25) are specified as references in figuring spatial pattern and error of GCM. Statistical bias-correction scheme comprises improvements of three main flaws of GCM precipitation such as low intensity drizzled rain days with no dry day, underestimation of heavy rainfall and inter-annual variability of local climate. Biases of heavy rainfall are conducted by generalized Pareto distribution (GPD) fitting over a peak over threshold series. Frequency of rain day error is fixed by rank order statistics and seasonal variation problem is solved by using a gamma distribution fitting in each month against insi-tu stations vs. corresponding GCM grids. By implementing the proposed bias-correction technique to all insi-tu stations and their respective GCM grid, an easy and effective downscaling process for impact studies at the basin scale is accomplished. The proposed method have been examined its applicability to some of the basins in various climate

  18. Bias in the physical examination of patients with lumbar radiculopathy

    Directory of Open Access Journals (Sweden)

    Katz Jeffrey N

    2010-11-01

    Full Text Available Abstract Background No prior studies have examined systematic bias in the musculoskeletal physical examination. The objective of this study was to assess the effects of bias due to prior knowledge of lumbar spine magnetic resonance imaging findings (MRI on perceived diagnostic accuracy of the physical examination for lumbar radiculopathy. Methods This was a cross-sectional comparison of the performance characteristics of the physical examination with blinding to MRI results (the 'independent group' with performance in the situation where the physical examination was not blinded to MRI results (the 'non-independent group'. The reference standard was the final diagnostic impression of nerve root impingement by the examining physician. Subjects were recruited from a hospital-based outpatient specialty spine clinic. All adults age 18 and older presenting with lower extremity radiating pain of duration ≤ 12 weeks were evaluated for participation. 154 consecutively recruited subjects with lumbar disk herniation confirmed by lumbar spine MRI were included in this study. Sensitivities and specificities with 95% confidence intervals were calculated in the independent and non-independent groups for the four components of the radiculopathy examination: 1 provocative testing, 2 motor strength testing, 3 pinprick sensory testing, and 4 deep tendon reflex testing. Results The perceived sensitivity of sensory testing was higher with prior knowledge of MRI results (20% vs. 36%; p = 0.05. Sensitivities and specificities for exam components otherwise showed no statistically significant differences between groups. Conclusions Prior knowledge of lumbar MRI results may introduce bias into the pinprick sensory testing component of the physical examination for lumbar radiculopathy. No statistically significant effect of bias was seen for other components of the physical examination. The effect of bias due to prior knowledge of lumbar MRI results should be considered

  19. Bias in the physical examination of patients with lumbar radiculopathy

    Science.gov (United States)

    2010-01-01

    Background No prior studies have examined systematic bias in the musculoskeletal physical examination. The objective of this study was to assess the effects of bias due to prior knowledge of lumbar spine magnetic resonance imaging findings (MRI) on perceived diagnostic accuracy of the physical examination for lumbar radiculopathy. Methods This was a cross-sectional comparison of the performance characteristics of the physical examination with blinding to MRI results (the 'independent group') with performance in the situation where the physical examination was not blinded to MRI results (the 'non-independent group'). The reference standard was the final diagnostic impression of nerve root impingement by the examining physician. Subjects were recruited from a hospital-based outpatient specialty spine clinic. All adults age 18 and older presenting with lower extremity radiating pain of duration ≤ 12 weeks were evaluated for participation. 154 consecutively recruited subjects with lumbar disk herniation confirmed by lumbar spine MRI were included in this study. Sensitivities and specificities with 95% confidence intervals were calculated in the independent and non-independent groups for the four components of the radiculopathy examination: 1) provocative testing, 2) motor strength testing, 3) pinprick sensory testing, and 4) deep tendon reflex testing. Results The perceived sensitivity of sensory testing was higher with prior knowledge of MRI results (20% vs. 36%; p = 0.05). Sensitivities and specificities for exam components otherwise showed no statistically significant differences between groups. Conclusions Prior knowledge of lumbar MRI results may introduce bias into the pinprick sensory testing component of the physical examination for lumbar radiculopathy. No statistically significant effect of bias was seen for other components of the physical examination. The effect of bias due to prior knowledge of lumbar MRI results should be considered when an isolated

  20. Bias modification training can alter approach bias and chocolate consumption.

    Science.gov (United States)

    Schumacher, Sophie E; Kemps, Eva; Tiggemann, Marika

    2016-01-01

    Recent evidence has demonstrated that bias modification training has potential to reduce cognitive biases for attractive targets and affect health behaviours. The present study investigated whether cognitive bias modification training could be applied to reduce approach bias for chocolate and affect subsequent chocolate consumption. A sample of 120 women (18-27 years) were randomly assigned to an approach-chocolate condition or avoid-chocolate condition, in which they were trained to approach or avoid pictorial chocolate stimuli, respectively. Training had the predicted effect on approach bias, such that participants trained to approach chocolate demonstrated an increased approach bias to chocolate stimuli whereas participants trained to avoid such stimuli showed a reduced bias. Further, participants trained to avoid chocolate ate significantly less of a chocolate muffin in a subsequent taste test than participants trained to approach chocolate. Theoretically, results provide support for the dual process model's conceptualisation of consumption as being driven by implicit processes such as approach bias. In practice, approach bias modification may be a useful component of interventions designed to curb the consumption of unhealthy foods. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. A re-examination of the effects of biased lineup instructions in eyewitness identification.

    Science.gov (United States)

    Clark, Steven E

    2005-10-01

    A meta-analytic review of research comparing biased and unbiased instructions in eyewitness identification experiments showed an asymmetry; specifically, that biased instructions led to a large and consistent decrease in accuracy in target-absent lineups, but produced inconsistent results for target-present lineups, with an average effect size near zero (Steblay, 1997). The results for target-present lineups are surprising, and are inconsistent with statistical decision theories (i.e., Green & Swets, 1966). A re-examination of the relevant studies and the meta-analysis of those studies shows clear evidence that correct identification rates do increase with biased lineup instructions, and that biased witnesses make correct identifications at a rate considerably above chance. Implications for theory, as well as police procedure and policy, are discussed.

  2. Addressing the mischaracterization of extreme rainfall in regional climate model simulations - A synoptic pattern based bias correction approach

    Science.gov (United States)

    Li, Jingwan; Sharma, Ashish; Evans, Jason; Johnson, Fiona

    2018-01-01

    Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.

  3. The weaker sex? The propensity for male-biased piglet mortality.

    Directory of Open Access Journals (Sweden)

    Emma M Baxter

    Full Text Available For the most part solutions to farm animal welfare issues, such as piglet mortality, are likely to lie within the scientific disciplines of environmental design and genetic selection, however understanding the ecological basis of some of the complex dynamics observed between parent and offspring could make a valuable contribution. One interesting, and often discussed, aspect of mortality is the propensity for it to be sex-biased. This study investigated whether known physiological and behavioural indicators of piglet survival differed between the sexes and whether life history strategies (often reported in wild or feral populations relating to parental investment were being displayed in a domestic population of pigs. Sex ratio (proportion of males (males/males+females at birth was 0.54 and sex allocation (maternal investment measured as piglet birth weight/litter weight was statistically significantly male-biased at 0.55 (t(35 = 2.51 P = 0.017, suggesting that sows invested more in sons than daughters during gestation. Despite this investment in birth weight, a known survival indicator, total pre-weaning male mortality was statistically significantly higher than female mortality (12% vs. 7% respectively z = 2.06 P = 0.040. Males tended to suffer from crushing by the sow more than females and statistically significantly more males died from disease-related causes. Although males were born on average heavier, with higher body mass index and ponderal index, these differences were not sustained. In addition male piglets showed impaired thermoregulation compared to females. These results suggest male-biased mortality exists despite greater initial maternal investment, and therefore reflects the greater susceptibility of this sex to causal mortality factors. Life history strategies are being displayed by a domestic population of pigs with sows in this study displaying a form of parental optimism by allocating greater resources at birth

  4. Combination of biased forecasts: Bias correction or bias based weights?

    OpenAIRE

    Wenzel, Thomas

    1999-01-01

    Most of the literature on combination of forecasts deals with the assumption of unbiased individual forecasts. Here, we consider the case of biased forecasts and discuss two different combination techniques resulting in an unbiased forecast. On the one hand we correct the individual forecasts, and on the other we calculate bias based weights. A simulation study gives some insight in the situations where we should use the different methods.

  5. Estimation bias and bias correction in reduced rank autoregressions

    DEFF Research Database (Denmark)

    Nielsen, Heino Bohn

    2017-01-01

    This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...

  6. Moisture Forecast Bias Correction in GEOS DAS

    Science.gov (United States)

    Dee, D.

    1999-01-01

    Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

  7. QUANTIFYING THE BIASES OF SPECTROSCOPICALLY SELECTED GRAVITATIONAL LENSES

    International Nuclear Information System (INIS)

    Arneson, Ryan A.; Brownstein, Joel R.; Bolton, Adam S.

    2012-01-01

    Spectroscopic selection has been the most productive technique for the selection of galaxy-scale strong gravitational lens systems with known redshifts. Statistically significant samples of strong lenses provide a powerful method for measuring the mass-density parameters of the lensing population, but results can only be generalized to the parent population if the lensing selection biases are sufficiently understood. We perform controlled Monte Carlo simulations of spectroscopic lens surveys in order to quantify the bias of lenses relative to parent galaxies in velocity dispersion, mass axis ratio, and mass-density profile. For parameters typical of the SLACS and BELLS surveys, we find (1) no significant mass axis ratio detection bias of lenses relative to parent galaxies; (2) a very small detection bias toward shallow mass-density profiles, which is likely negligible compared to other sources of uncertainty in this parameter; (3) a detection bias toward smaller Einstein radius for systems drawn from parent populations with group- and cluster-scale lensing masses; and (4) a lens-modeling bias toward larger velocity dispersions for systems drawn from parent samples with sub-arcsecond mean Einstein radii. This last finding indicates that the incorporation of velocity-dispersion upper limits of non-lenses is an important ingredient for unbiased analyses of spectroscopically selected lens samples. In general, we find that the completeness of spectroscopic lens surveys in the plane of Einstein radius and mass-density profile power-law index is quite uniform, up to a sharp drop in the region of large Einstein radius and steep mass-density profile, and hence that such surveys are ideally suited to the study of massive field galaxies.

  8. Generation of future potential scenarios in an Alpine Catchment by applying bias-correction techniques, delta-change approaches and stochastic Weather Generators at different spatial scale. Analysis of their influence on basic and drought statistics.

    Science.gov (United States)

    Collados-Lara, Antonio-Juan; Pulido-Velazquez, David; Pardo-Iguzquiza, Eulogio

    2017-04-01

    Assessing impacts of potential future climate change scenarios in precipitation and temperature is essential to design adaptive strategies in water resources systems. The objective of this work is to analyze the possibilities of different statistical downscaling methods to generate future potential scenarios in an Alpine Catchment from historical data and the available climate models simulations performed in the frame of the CORDEX EU project. The initial information employed to define these downscaling approaches are the historical climatic data (taken from the Spain02 project for the period 1971-2000 with a spatial resolution of 12.5 Km) and the future series provided by climatic models in the horizon period 2071-2100 . We have used information coming from nine climate model simulations (obtained from five different Regional climate models (RCM) nested to four different Global Climate Models (GCM)) from the European CORDEX project. In our application we have focused on the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC). For each RCM we have generated future climate series for the period 2071-2100 by applying two different approaches, bias correction and delta change, and five different transformation techniques (first moment correction, first and second moment correction, regression functions, quantile mapping using distribution derived transformation and quantile mapping using empirical quantiles) for both of them. Ensembles of the obtained series were proposed to obtain more representative potential future climate scenarios to be employed to study potential impacts. In this work we propose a non-equifeaseble combination of the future series giving more weight to those coming from models (delta change approaches) or combination of models and techniques that provides better approximation to the basic

  9. The Bias in Bayes and How to Measure it

    Directory of Open Access Journals (Sweden)

    D.A. S. Fraser

    2012-07-01

    Full Text Available A Bayes prior with a likelihood can give approximate confidence and provide a remarkably flexible approach to statistical inference; but is also known to provide inaccurate perhaps incorrect results. We develop a measure of Bayes bias, first examining a simple Normal model and then progressing to quite general models with scalar and vector parameters. The Bias measure can be interpreted as the lateral displacement of the location standardized likelihood function and thus provides ready access to the effect of a prior on p-values, confidence bounds, and Bayes posterior bounds. The needed computation is comparable to that for the likelihood function and thus provides an initial option for checking merits of Bayesian computation for high dimensions.

  10. Evaluation of bias and variance in low-count OSEM list mode reconstruction

    International Nuclear Information System (INIS)

    Jian, Y; Carson, R E; Planeta, B

    2015-01-01

    Statistical algorithms have been widely used in PET image reconstruction. The maximum likelihood expectation maximization reconstruction has been shown to produce bias in applications where images are reconstructed from a relatively small number of counts. In this study, image bias and variability in low-count OSEM reconstruction are investigated on images reconstructed with MOLAR (motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction) platform. A human brain ([ 11 C]AFM) and a NEMA phantom are used in the simulation and real experiments respectively, for the HRRT and Biograph mCT. Image reconstructions were repeated with different combinations of subsets and iterations. Regions of interest were defined on low-activity and high-activity regions to evaluate the bias and noise at matched effective iteration numbers (iterations × subsets). Minimal negative biases and no positive biases were found at moderate count levels and less than 5% negative bias was found using extremely low levels of counts (0.2 M NEC). At any given count level, other factors, such as subset numbers and frame-based scatter correction may introduce small biases (1–5%) in the reconstructed images. The observed bias was substantially lower than that reported in the literature, perhaps due to the use of point spread function and/or other implementation methods in MOLAR. (paper)

  11. The Use of Official Statistics in Self-Selection Bias Modeling

    OpenAIRE

    Dalla Valle, L

    2016-01-01

    Official statistics are a fundamental source of publicly available information that periodically provides a great amount of data on all major areas of citizens’ lives, such as economics, social development, education, and the environment. However, these extraordinary sources of information are often neglected, especially by business and industrial statisticians. In particular, data collected from small businesses, like small and medium-sized enterprizes (SMEs), are rarely integrated with offi...

  12. Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

    Science.gov (United States)

    Beria, H.; Nanda, T., Sr.; Chatterjee, C.

    2015-12-01

    High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

  13. Assessment of whole genome amplification-induced bias through high-throughput, massively parallel whole genome sequencing

    Directory of Open Access Journals (Sweden)

    Plant Ramona N

    2006-08-01

    Full Text Available Abstract Background Whole genome amplification is an increasingly common technique through which minute amounts of DNA can be multiplied to generate quantities suitable for genetic testing and analysis. Questions of amplification-induced error and template bias generated by these methods have previously been addressed through either small scale (SNPs or large scale (CGH array, FISH methodologies. Here we utilized whole genome sequencing to assess amplification-induced bias in both coding and non-coding regions of two bacterial genomes. Halobacterium species NRC-1 DNA and Campylobacter jejuni were amplified by several common, commercially available protocols: multiple displacement amplification, primer extension pre-amplification and degenerate oligonucleotide primed PCR. The amplification-induced bias of each method was assessed by sequencing both genomes in their entirety using the 454 Sequencing System technology and comparing the results with those obtained from unamplified controls. Results All amplification methodologies induced statistically significant bias relative to the unamplified control. For the Halobacterium species NRC-1 genome, assessed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 119 times greater than those from unamplified material, 164.0 times greater for Repli-G, 165.0 times greater for PEP-PCR and 252.0 times greater than the unamplified controls for DOP-PCR. For Campylobacter jejuni, also analyzed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 15 times greater than those from unamplified material, 19.8 times greater for Repli-G, 61.8 times greater for PEP-PCR and 220.5 times greater than the unamplified controls for DOP-PCR. Conclusion Of the amplification methodologies examined in this paper, the multiple displacement amplification products generated the least bias, and produced significantly higher yields of amplified DNA.

  14. Critical Views of 8th Grade Students toward Statistical Data in Newspaper Articles: Analysis in Light of Statistical Literacy

    Science.gov (United States)

    Guler, Mustafa; Gursoy, Kadir; Guven, Bulent

    2016-01-01

    Understanding and interpreting biased data, decision-making in accordance with the data, and critically evaluating situations involving data are among the fundamental skills necessary in the modern world. To develop these required skills, emphasis on statistical literacy in school mathematics has been gradually increased in recent years. The…

  15. Modeling bias and variation in the stochastic processes of small RNA sequencing.

    Science.gov (United States)

    Argyropoulos, Christos; Etheridge, Alton; Sakhanenko, Nikita; Galas, David

    2017-06-20

    The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  16. No arousal-biased competition in focused visuospatial attention.

    Science.gov (United States)

    Ásgeirsson, Árni Gunnar; Nieuwenhuis, Sander

    2017-11-01

    Arousal sometimes enhances and sometimes impairs perception and memory. A recent theory attempts to reconcile these findings by proposing that arousal amplifies the competition between stimulus representations, strengthening already strong representations and weakening already weak representations. Here, we report a stringent test of this arousal-biased competition theory in the context of focused visuospatial attention. Participants were required to identify a briefly presented target in the context of multiple distractors, which varied in the degree to which they competed for representation with the target, as revealed by psychophysics. We manipulated arousal using emotionally arousing pictures (Experiment 1), alerting tones (Experiment 2) and white-noise stimulation (Experiment 3), and validated these manipulations with electroencephalography and pupillometry. In none of the experiments did we find evidence that arousal modulated the effect of distractor competition on the accuracy of target identification. Bayesian statistics revealed moderate to strong evidence against arousal-biased competition. Modeling of the psychophysical data based on Bundesen's (1990) theory of visual attention corroborated the conclusion that arousal does not bias competition in focused visuospatial attention. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Journal bias or author bias?

    Science.gov (United States)

    Harris, Ian

    2016-01-01

    I read with interest the comment by Mark Wilson in the Indian Journal of Medical Ethics regarding bias and conflicts of interest in medical journals. Wilson targets one journal (the New England Journal of Medicine: NEJM) and one particular "scandal" to make his point that journals' decisions on publication are biased by commercial conflicts of interest (CoIs). It is interesting that he chooses the NEJM which, by his own admission, had one of the strictest CoI policies and had published widely on this topic. The feeling is that if the NEJM can be guilty, they can all be guilty.

  18. Stochastic bias-correction of daily rainfall scenarios for hydrological applications

    Directory of Open Access Journals (Sweden)

    I. Portoghese

    2011-09-01

    Full Text Available The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge.

    In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.

  19. Detection of bias in animal model pedigree indices of heifers

    Directory of Open Access Journals (Sweden)

    M. LIDAUER

    2008-12-01

    Full Text Available The objective of the study was to test whether the pedigree indices (PI of heifers are biased, and if so, whether the magnitude of the bias varies in different groups of heifers. Therefore, two animal model evaluations with two different data sets were computed. Data with all the records from the national evaluation in December 1994 was used to obtain estimated breeding values (EBV for 305-days' milk yield and protein yield. In the second evaluation, the PIs were estimated for cows calving the first time in 1993 by excluding all their production records from the data. Three different statistics, a simple t-test, the linear regression of EBV on PI, and the polynomial regression of the difference in the predictions (EBV-PI on PI, were computed for three groups of first parity Ayrshire cows: daughters of proven sires, daughters of young sires, and daughters of bull dam candidates. A practically relevant bias was found only in the PIs for the daughters of young sires. On average their PIs were biased upwards by 0.20 standard deviations (78.8 kg for the milk yield and by 0.21 standard deviations (2.2 kg for the protein yield. The polynomial regression analysis showed that the magnitude of the bias in the PIs changed somewhat with the size of the PIs.;

  20. Accuracy and detection limits for bioassay measurements in radiation protection. Statistical considerations

    International Nuclear Information System (INIS)

    Brodsky, A.

    1986-04-01

    This report provides statistical concepts and formulas for defining minimum detectable amount (MDA), bias and precision of sample analytical measurements of radioactivity for radiobioassay purposes. The defined statistical quantities and accuracy criteria were developed for use in standard performance criteria for radiobioassay, but are also useful in intralaboratory quality assurance programs. This report also includes a literature review and analysis of accuracy needs and accuracy recommendations of national and international scientific organizations for radiation or radioactivity measurements used for radiation protection purposes. Computer programs are also included for calculating the probabilities of passing or failing multiple analytical tests for different acceptable ranges of bias and precision

  1. The Detection and Correction of Bias in Student Ratings of Instruction.

    Science.gov (United States)

    Haladyna, Thomas; Hess, Robert K.

    1994-01-01

    A Rasch model was used to detect and correct bias in Likert rating scales used to assess student perceptions of college teaching, using a database of ratings. Statistical corrections were significant, supporting the model's potential utility. Recommendations are made for a theoretical rationale and further research on the model. (Author/MSE)

  2. Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2018-02-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  3. Mask effects on cosmological studies with weak-lensing peak statistics

    International Nuclear Information System (INIS)

    Liu, Xiangkun; Pan, Chuzhong; Fan, Zuhui; Wang, Qiao

    2014-01-01

    With numerical simulations, we analyze in detail how the bad data removal, i.e., the mask effect, can influence the peak statistics of the weak-lensing convergence field reconstructed from the shear measurement of background galaxies. It is found that high peak fractions are systematically enhanced because of the presence of masks; the larger the masked area is, the higher the enhancement is. In the case where the total masked area is about 13% of the survey area, the fraction of peaks with signal-to-noise ratio ν ≥ 3 is ∼11% of the total number of peaks, compared with ∼7% of the mask-free case in our considered cosmological model. This can have significant effects on cosmological studies with weak-lensing convergence peak statistics, inducing a large bias in the parameter constraints if the effects are not taken into account properly. Even for a survey area of 9 deg 2 , the bias in (Ω m , σ 8 ) is already intolerably large and close to 3σ. It is noted that most of the affected peaks are close to the masked regions. Therefore, excluding peaks in those regions in the peak statistics can reduce the bias effect but at the expense of losing usable survey areas. Further investigations find that the enhancement of the number of high peaks around the masked regions can be largely attributed to the smaller number of galaxies usable in the weak-lensing convergence reconstruction, leading to higher noise than that of the areas away from the masks. We thus develop a model in which we exclude only those very large masks with radius larger than 3' but keep all the other masked regions in peak counting statistics. For the remaining part, we treat the areas close to and away from the masked regions separately with different noise levels. It is shown that this two-noise-level model can account for the mask effect on peak statistics very well, and the bias in cosmological parameters is significantly reduced if this model is applied in the parameter fitting.

  4. Angular biasing in implicit Monte-Carlo

    International Nuclear Information System (INIS)

    Zimmerman, G.B.

    1994-01-01

    Calculations of indirect drive Inertial Confinement Fusion target experiments require an integrated approach in which laser irradiation and radiation transport in the hohlraum are solved simultaneously with the symmetry, implosion and burn of the fuel capsule. The Implicit Monte Carlo method has proved to be a valuable tool for the two dimensional radiation transport within the hohlraum, but the impact of statistical noise on the symmetric implosion of the small fuel capsule is difficult to overcome. We present an angular biasing technique in which an increased number of low weight photons are directed at the imploding capsule. For typical parameters this reduces the required computer time for an integrated calculation by a factor of 10. An additional factor of 5 can also be achieved by directing even smaller weight photons at the polar regions of the capsule where small mass zones are most sensitive to statistical noise

  5. RELIC: a novel dye-bias correction method for Illumina Methylation BeadChip.

    Science.gov (United States)

    Xu, Zongli; Langie, Sabine A S; De Boever, Patrick; Taylor, Jack A; Niu, Liang

    2017-01-03

    The Illumina Infinium HumanMethylation450 BeadChip and its successor, Infinium MethylationEPIC BeadChip, have been extensively utilized in epigenome-wide association studies. Both arrays use two fluorescent dyes (Cy3-green/Cy5-red) to measure methylation level at CpG sites. However, performance difference between dyes can result in biased estimates of methylation levels. Here we describe a novel method, called REgression on Logarithm of Internal Control probes (RELIC) to correct for dye bias on whole array by utilizing the intensity values of paired internal control probes that monitor the two color channels. We evaluate the method in several datasets against other widely used dye-bias correction methods. Results on data quality improvement showed that RELIC correction statistically significantly outperforms alternative dye-bias correction methods. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website ( https://www.bioconductor.org/packages/release/bioc/html/ENmix.html ). RELIC is an efficient and robust method to correct for dye-bias in Illumina Methylation BeadChip data. It outperforms other alternative methods and conveniently implemented in R package ENmix to facilitate DNA methylation studies.

  6. Targeted estimation of nuisance parameters to obtain valid statistical inference.

    Science.gov (United States)

    van der Laan, Mark J

    2014-01-01

    In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special

  7. Beyond assembly bias: exploring secondary halo biases for cluster-size haloes

    Science.gov (United States)

    Mao, Yao-Yuan; Zentner, Andrew R.; Wechsler, Risa H.

    2018-03-01

    Secondary halo bias, commonly known as `assembly bias', is the dependence of halo clustering on a halo property other than mass. This prediction of the Λ Cold Dark Matter cosmology is essential to modelling the galaxy distribution to high precision and interpreting clustering measurements. As the name suggests, different manifestations of secondary halo bias have been thought to originate from halo assembly histories. We show conclusively that this is incorrect for cluster-size haloes. We present an up-to-date summary of secondary halo biases of high-mass haloes due to various halo properties including concentration, spin, several proxies of assembly history, and subhalo properties. While concentration, spin, and the abundance and radial distribution of subhaloes exhibit significant secondary biases, properties that directly quantify halo assembly history do not. In fact, the entire assembly histories of haloes in pairs are nearly identical to those of isolated haloes. In general, a global correlation between two halo properties does not predict whether or not these two properties exhibit similar secondary biases. For example, assembly history and concentration (or subhalo abundance) are correlated for both paired and isolated haloes, but follow slightly different conditional distributions in these two cases. This results in a secondary halo bias due to concentration (or subhalo abundance), despite the lack of assembly bias in the strict sense for cluster-size haloes. Due to this complexity, caution must be exercised in using any one halo property as a proxy to study the secondary bias due to another property.

  8. Natural-gas futures: Bias, predictive performance, and the theory of storage

    International Nuclear Information System (INIS)

    Modjtahedi, Bagher; Movassagh, Nahid

    2005-01-01

    This study reports several empirical findings concerning natural gas futures prices. First, spot and futures prices are non-stationary and the observed trends are due to positive drifts in the random-walk components of the prices rather than possible deterministic time trends. Second, market forecast errors are stationary. Third, futures are less than expected future spot prices so that futures are backdated. Fourth, the bias in the futures prices is time varying. Fifth, futures have statistically significant market-timing ability, despite the bias in the magnitude forecasts. Finally, the data lends partial support to the cost-of-carry theory of the basis determination. (Author)

  9. Natural-gas futures: Bias, predictive performance, and the theory of storage

    Energy Technology Data Exchange (ETDEWEB)

    Modjtahedi, Bagher [California Univ., Davis, CA (United States); California Franchise Tax Board, CA (United States); Movassagh, Nahid [California Energy Commission, MS22, Sacramento, CA (United States)

    2005-07-01

    This study reports several empirical findings concerning natural gas futures prices. First, spot and futures prices are non-stationary and the observed trends are due to positive drifts in the random-walk components of the prices rather than possible deterministic time trends. Second, market forecast errors are stationary. Third, futures are less than expected future spot prices so that futures are backdated. Fourth, the bias in the futures prices is time varying. Fifth, futures have statistically significant market-timing ability, despite the bias in the magnitude forecasts. Finally, the data lends partial support to the cost-of-carry theory of the basis determination. (Author)

  10. Attentional bias mediates the effect of neurostimulation on emotional vulnerability.

    Science.gov (United States)

    Chen, Nigel T M; Basanovic, Julian; Notebaert, Lies; MacLeod, Colin; Clarke, Patrick J F

    2017-10-01

    Transcranial direct current stimulation (tDCS) is a neuromodulatory technique which has garnered recent interest in the potential treatment for emotion-based psychopathology. While accumulating evidence suggests that tDCS may attenuate emotional vulnerability, critically, little is known about underlying mechanisms of this effect. The present study sought to clarify this by examining the possibility that tDCS may affect emotional vulnerability via its capacity to modulate attentional bias towards threatening information. Fifty healthy participants were randomly assigned to receive either anodal tDCS (2 mA/min) stimulation to the left dorsolateral prefrontal cortex (DLPFC), or sham. Participants were then eye tracked during a dual-video stressor task designed to elicit emotional reactivity, while providing a concurrent in-vivo measure of attentional bias. Greater attentional bias towards threatening information was associated with greater emotional reactivity to the stressor task. Furthermore, the active tDCS group showed reduced attentional bias to threat, compared to the sham group. Importantly, attentional bias was found to statistically mediate the effect of tDCS on emotional reactivity, while no direct effect of tDCS on emotional reactivity was observed. The findings are consistent with the notion that the effect of tDCS on emotional vulnerability may be mediated by changes in attentional bias, holding implications for the application of tDCS in emotion-based psychopathology. The findings also highlight the utility of in-vivo eye tracking measures in the examination of the mechanisms associated with DLPFC neuromodulation in emotional vulnerability. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Correcting estimators of theta and Tajima's D for ascertainment biases caused by the single-nucleotide polymorphism discovery process

    DEFF Research Database (Denmark)

    Ramírez-Soriano, Anna; Nielsen, Rasmus

    2009-01-01

    Most single-nucleotide polymorphism (SNP) data suffer from an ascertainment bias caused by the process of SNP discovery followed by SNP genotyping. The final genotyped data are biased toward an excess of common alleles compared to directly sequenced data, making standard genetic methods of analysis...... the variances and covariances of these estimators and provide a corrected version of Tajima's D statistic. We reanalyze a human genomewide SNP data set and find substantial differences in the results with or without ascertainment bias correction....

  12. Long-branch attraction bias and inconsistency in Bayesian phylogenetics.

    Science.gov (United States)

    Kolaczkowski, Bryan; Thornton, Joseph W

    2009-12-09

    Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias--which is apparent under both controlled simulation conditions and in analyses of empirical sequence data--also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages--that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis.

  13. CPI Bias in Korea

    Directory of Open Access Journals (Sweden)

    Chul Chung

    2007-12-01

    Full Text Available We estimate the CPI bias in Korea by employing the approach of Engel’s Law as suggested by Hamilton (2001. This paper is the first attempt to estimate the bias using Korean panel data, Korean Labor and Income Panel Study(KLIPS. Following Hamilton’s model with non­linear specification correction, our estimation result shows that the cumulative CPI bias over the sample period (2000-2005 was 0.7 percent annually. This CPI bias implies that about 21 percent of the inflation rate during the period can be attributed to the bias. In light of purchasing power parity, we provide an interpretation of the estimated bias.

  14. HMO marketing and selection bias: are TEFRA HMOs skimming?

    Science.gov (United States)

    Lichtenstein, R; Thomas, J W; Watkins, B; Puto, C; Lepkowski, J; Adams-Watson, J; Simone, B; Vest, D

    1992-04-01

    The research evidence indicates that health maintenance organizations (HMOs) participating in the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) At-Risk Program tend to experience favorable selection. Although favorable selection might result from patient decisions, a common conjecture is that it can be induced by HMOs through their marketing activities. The purpose of this study is to examine the relationship between HMO marketing strategies and selection bias in TEFRA At-Risk HMOs. A purposive sample of 22 HMOs that were actively marketing their TEFRA programs was selected and data on organizational characteristics, market area characteristics, and HMO marketing decisions were collected. To measure selection bias in these HMOs, the functional health status of approximately 300 enrollees in each HMO was compared to that of 300 non-enrolling beneficiaries in the same area. Three dependent variables, reflecting selection bias at the mean, the low health tail, and the high health tail of the health status distribution were created. Weighted least squares regressions were then used to identify relationships between marketing elements and selection bias. Subject to the statistical limitations of the study, our conclusion is that it is doubtful that HMO marketing decisions are responsible for the prevalence of favorable selection in HMO enrollment. It also appears unlikely that HMOs were differentially targeting healthy and unhealthy segments of the Medicare market.

  15. Potential Reporting Bias in Neuroimaging Studies of Sex Differences.

    Science.gov (United States)

    David, Sean P; Naudet, Florian; Laude, Jennifer; Radua, Joaquim; Fusar-Poli, Paolo; Chu, Isabella; Stefanick, Marcia L; Ioannidis, John P A

    2018-04-17

    Numerous functional magnetic resonance imaging (fMRI) studies have reported sex differences. To empirically evaluate for evidence of excessive significance bias in this literature, we searched for published fMRI studies of human brain to evaluate sex differences, regardless of the topic investigated, in Medline and Scopus over 10 years. We analyzed the prevalence of conclusions in favor of sex differences and the correlation between study sample sizes and number of significant foci identified. In the absence of bias, larger studies (better powered) should identify a larger number of significant foci. Across 179 papers, median sample size was n = 32 (interquartile range 23-47.5). A median of 5 foci related to sex differences were reported (interquartile range, 2-9.5). Few articles (n = 2) had titles focused on no differences or on similarities (n = 3) between sexes. Overall, 158 papers (88%) reached "positive" conclusions in their abstract and presented some foci related to sex differences. There was no statistically significant relationship between sample size and the number of foci (-0.048% increase for every 10 participants, p = 0.63). The extremely high prevalence of "positive" results and the lack of the expected relationship between sample size and the number of discovered foci reflect probable reporting bias and excess significance bias in this literature.

  16. Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association

    Directory of Open Access Journals (Sweden)

    Kelsey E. Grinde

    2017-09-01

    Full Text Available To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p < 2.2 × 10−6 and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures.

  17. Analysis of the theoretical bias in dark matter direct detection

    International Nuclear Information System (INIS)

    Catena, Riccardo

    2014-01-01

    Fitting the model ''A'' to dark matter direct detection data, when the model that underlies the data is ''B'', introduces a theoretical bias in the fit. We perform a quantitative study of the theoretical bias in dark matter direct detection, with a focus on assumptions regarding the dark matter interactions, and velocity distribution. We address this problem within the effective theory of isoscalar dark matter-nucleon interactions mediated by a heavy spin-1 or spin-0 particle. We analyze 24 benchmark points in the parameter space of the theory, using frequentist and Bayesian statistical methods. First, we simulate the data of future direct detection experiments assuming a momentum/velocity dependent dark matter-nucleon interaction, and an anisotropic dark matter velocity distribution. Then, we fit a constant scattering cross section, and an isotropic Maxwell-Boltzmann velocity distribution to the simulated data, thereby introducing a bias in the analysis. The best fit values of the dark matter particle mass differ from their benchmark values up to 2 standard deviations. The best fit values of the dark matter-nucleon coupling constant differ from their benchmark values up to several standard deviations. We conclude that common assumptions in dark matter direct detection are a source of potentially significant bias

  18. Infrared Resummation for Biased Tracers in Redshift Space arXiv

    CERN Document Server

    Ivanov, Mikhail M.

    We incorporate the effects of redshift space distortions and non-linear bias in time-sliced perturbation theory (TSPT). This is done via a new method that allows to map cosmological correlation functions from real to redshift space. This mapping preserves a transparent infrared (IR) structure of the theory and provides us with an efficient tool to study non-linear infrared effects altering the pattern of baryon acoustic oscillations (BAO) in redshift space. We give an accurate description of the BAO by means of a systematic resummation of Feynman diagrams guided by well-defined power counting rules. This establishes IR resummation within TSPT as a robust and complete procedure and provides a consistent theoretical model for the BAO feature in the statistics of biased tracers in redshift space.

  19. Robust Control Methods for On-Line Statistical Learning

    Directory of Open Access Journals (Sweden)

    Capobianco Enrico

    2001-01-01

    Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.

  20. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.

    Science.gov (United States)

    Vanderweele, Tyler J; Arah, Onyebuchi A

    2011-01-01

    Uncontrolled confounding in observational studies gives rise to biased effect estimates. Sensitivity analysis techniques can be useful in assessing the magnitude of these biases. In this paper, we use the potential outcomes framework to derive a general class of sensitivity-analysis formulas for outcomes, treatments, and measured and unmeasured confounding variables that may be categorical or continuous. We give results for additive, risk-ratio and odds-ratio scales. We show that these results encompass a number of more specific sensitivity-analysis methods in the statistics and epidemiology literature. The applicability, usefulness, and limits of the bias-adjustment formulas are discussed. We illustrate the sensitivity-analysis techniques that follow from our results by applying them to 3 different studies. The bias formulas are particularly simple and easy to use in settings in which the unmeasured confounding variable is binary with constant effect on the outcome across treatment levels.

  1. Bias and misleading concepts in an Arnica research study. Comments to improve experimental Homeopathy

    Directory of Open Access Journals (Sweden)

    Salvatore Chirumbolo

    2018-01-01

    Full Text Available Basic experimental models in Homeopathy are of major interest because they could get insightful data about the ability of high dilutions to work in a biological system. Due to the extreme difficulty in the highlighting any possible effect and trusting its reliability, methods should be particularly stringent and highly standardized. Confounders, handling process, pre-analytical errors, misleading statistics and misinterpretations may lead to experimental biases. This article tries to elucidate those factors causing bias, taking into account some recent reported evidence in the field.

  2. EVALUATING GENDER-BIAS IN THE IRANIAN PRE-UNIVERSITY ENGLISH TEXTBOOKS

    Directory of Open Access Journals (Sweden)

    Ali Roohani

    2013-07-01

    Full Text Available Abstract: Textbooks can affect learners’ attitudes, viewpoints, and their choice of language in second/foreign language (L2 communication. The various ways in which the people are displayed in communication may affect students’ viewpoints. This study was an attempt to examine several potential areas of gender-bias in the representation of women and men in the pre-university English textbook, an English language teaching (ELT textbook with two volumes taught in the high schools of Iran. To evaluate this textbook, content analysis was done in terms of gender-bias, gender-neutral, male-generics, and the firstness in the reading texts, instructions, exercises and illustrations. The frequency and percentages of names, nouns, pronouns and pictures attributed to each gender (i.e., males and females were obtained and chi-square tests were carried out. In general, names, nouns and pictures pertinent to males outnumbered those attributed to females. Besides, there was a statistically significant difference between males and females in the two volumes of the pre-university textbook in terms of the above features. Also, male-oriented terms came first more frequently. However, there was not a statistically significant difference between male and female pronouns between the two volumes, though the percentage of male pronouns was higher. Moreover, the textbook included many gender-neutral nouns and pronouns not having any gender orientations. Findings indicated that the Iranian pre-university English textbook was somehow male-oriented and gender-biased as regards names, nouns, firstness and pictures associated with them though great efforts were made to avoid specific gender orientations.

  3. Approximate Bias Correction in Econometrics

    OpenAIRE

    James G. MacKinnon; Anthony A. Smith Jr.

    1995-01-01

    This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several evaluations of the bias function will be required to do this. Unfortunately, reducing bias may increase the variance, or even the mea...

  4. Evidence of selective reporting bias in hematology journals: A systematic review.

    Science.gov (United States)

    Wayant, Cole; Scheckel, Caleb; Hicks, Chandler; Nissen, Timothy; Leduc, Linda; Som, Mousumi; Vassar, Matt

    2017-01-01

    Selective reporting bias occurs when chance or selective outcome reporting rather than the intervention contributes to group differences. The prevailing concern about selective reporting bias is the possibility of results being modified towards specific conclusions. In this study, we evaluate randomized controlled trials (RCTs) published in hematology journals, a group in which selective outcome reporting has not yet been explored. Our primary goal was to examine discrepancies between the reported primary and secondary outcomes in registered and published RCTs concerning hematological malignancies reported in hematology journals with a high impact factor. The secondary goals were to address whether outcome reporting discrepancies favored statistically significant outcomes, whether a pattern existed between the funding source and likelihood of outcome reporting bias, and whether temporal trends were present in outcome reporting bias. For trials with major outcome discrepancies, we contacted trialists to determine reasons for these discrepancies. Trials published between January 1, 2010 and December 31, 2015 in Blood; British Journal of Haematology; American Journal of Hematology; Leukemia; and Haematologica were included. Of 499 RCTs screened, 109 RCTs were included. Our analysis revealed 118 major discrepancies and 629 total discrepancies. Among the 118 discrepancies, 30 (25.4%) primary outcomes were demoted, 47 (39.8%) primary outcomes were omitted, and 30 (25.4%) primary outcomes were added. Three (2.5%) secondary outcomes were upgraded to a primary outcome. The timing of assessment for a primary outcome changed eight (6.8%) times. Thirty-one major discrepancies were published with a P-value and twenty-five (80.6%) favored statistical significance. A majority of authors whom we contacted cited a pre-planned subgroup analysis as a reason for outcome changes. Our results suggest that outcome changes occur frequently in hematology trials. Because RCTs ultimately

  5. Apparatus bias and place conditioning with ethanol in mice.

    Science.gov (United States)

    Cunningham, Christopher L; Ferree, Nikole K; Howard, MacKenzie A

    2003-12-01

    ethanol was paired with the initially non-preferred cue, and not when it was paired with the initially preferred cue. These conclusions held regardless of which dependent variable was used to index place conditioning, but only if the counterbalancing factor was included in statistical analyses. These studies indicate that apparatus bias plays a major role in determining whether biased assignment of an ethanol-paired stimulus affects ability to demonstrate conditioned place preference. Ethanol's ability to produce conditioned place preference in an unbiased apparatus, regardless of the direction of the initial cue bias, supports previous studies that interpret such findings as evidence of a primary rewarding drug effect. Moreover, these studies suggest that the asymmetrical outcome observed in the biased apparatus is most likely due to a measurement problem (e.g., ceiling effect) rather than to an interaction between the drug's effect and an unconditioned motivational response (e.g., "anxiety") to the initially non-preferred stimulus. More generally, these findings illustrate the importance of providing clear information on apparatus bias in all place-conditioning studies.

  6. Attentional bias and disinhibition toward gaming cues are related to problem gaming in male adolescents.

    Science.gov (United States)

    van Holst, Ruth J; Lemmens, Jeroen S; Valkenburg, Patti M; Peter, Jochen; Veltman, Dick J; Goudriaan, Anna E

    2012-06-01

    The aim of this study was to examine whether behavioral tendencies commonly related to addictive behaviors are also related to problematic computer and video game playing in adolescents. The study of attentional bias and response inhibition, characteristic for addictive disorders, is relevant to the ongoing discussion on whether problematic gaming should be classified as an addictive disorder. We tested the relation between self-reported levels of problem gaming and two behavioral domains: attentional bias and response inhibition. Ninety-two male adolescents performed two attentional bias tasks (addiction-Stroop, dot-probe) and a behavioral inhibition task (go/no-go). Self-reported problem gaming was measured by the game addiction scale, based on the Diagnostic and Statistical Manual of Mental Disorders-fourth edition criteria for pathological gambling and time spent on computer and/or video games. Male adolescents with higher levels of self-reported problem gaming displayed signs of error-related attentional bias to game cues. Higher levels of problem gaming were also related to more errors on response inhibition, but only when game cues were presented. These findings are in line with the findings of attentional bias reported in clinically recognized addictive disorders, such as substance dependence and pathological gambling, and contribute to the discussion on the proposed concept of "Addiction and Related Disorders" (which may include non-substance-related addictive behaviors) in the Diagnostic and Statistical Manual of Mental Disorders-fourth edition. Copyright © 2012 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  7. A model independent safeguard against background mismodeling for statistical inference

    Energy Technology Data Exchange (ETDEWEB)

    Priel, Nadav; Landsman, Hagar; Manfredini, Alessandro; Budnik, Ranny [Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Herzl St. 234, Rehovot (Israel); Rauch, Ludwig, E-mail: nadav.priel@weizmann.ac.il, E-mail: rauch@mpi-hd.mpg.de, E-mail: hagar.landsman@weizmann.ac.il, E-mail: alessandro.manfredini@weizmann.ac.il, E-mail: ran.budnik@weizmann.ac.il [Teilchen- und Astroteilchenphysik, Max-Planck-Institut für Kernphysik, Saupfercheckweg 1, 69117 Heidelberg (Germany)

    2017-05-01

    We propose a safeguard procedure for statistical inference that provides universal protection against mismodeling of the background. The method quantifies and incorporates the signal-like residuals of the background model into the likelihood function, using information available in a calibration dataset. This prevents possible false discovery claims that may arise through unknown mismodeling, and corrects the bias in limit setting created by overestimated or underestimated background. We demonstrate how the method removes the bias created by an incomplete background model using three realistic case studies.

  8. Effects of model chemistry and data biases on stratospheric ozone assimilation

    Directory of Open Access Journals (Sweden)

    L. Coy

    2007-06-01

    Full Text Available The innovations or observation minus forecast (O–F residuals produced by a data assimilation system provide a convenient metric of evaluating global analyses. In this study, O–F statistics from the Global Ozone Assimilation Testing System (GOATS are used to examine how ozone assimilation products and their associated O–F statistics depend on input data biases and ozone photochemistry parameterizations (OPP. All the GOATS results shown are based on a 6-h forecast and analysis cycle using observations from SBUV/2 (Solar Backscatter UltraViolet instrument-2 during September–October 2002. Results show that zonal mean ozone analyses are more independent of observation biases and drifts when using an OPP, while the mean ozone O–Fs are more sensitive to observation drifts when using an OPP. In addition, SD O–Fs (standard deviations are reduced in the upper stratosphere when using an OPP due to a reduction of forecast model noise and to increased covariance between the forecast model and the observations. Experiments that changed the OPP reference state to match the observations by using an "adaptive" OPP scheme reduced the mean ozone O–Fs at the expense of zonal mean ozone analyses being more susceptible to data biases and drifts. Additional experiments showed that the upper boundary of the ozone DAS can affect the quality of the ozone analysis and therefore should be placed well above (at least a scale height the region of interest.

  9. Halo assembly bias and the tidal anisotropy of the local halo environment

    Science.gov (United States)

    Paranjape, Aseem; Hahn, Oliver; Sheth, Ravi K.

    2018-05-01

    We study the role of the local tidal environment in determining the assembly bias of dark matter haloes. Previous results suggest that the anisotropy of a halo's environment (i.e. whether it lies in a filament or in a more isotropic region) can play a significant role in determining the eventual mass and age of the halo. We statistically isolate this effect, using correlations between the large-scale and small-scale environments of simulated haloes at z = 0 with masses between 1011.6 ≲ (m/h-1 M⊙) ≲ 1014.9. We probe the large-scale environment, using a novel halo-by-halo estimator of linear bias. For the small-scale environment, we identify a variable αR that captures the tidal anisotropy in a region of radius R = 4R200b around the halo and correlates strongly with halo bias at fixed mass. Segregating haloes by αR reveals two distinct populations. Haloes in highly isotropic local environments (αR ≲ 0.2) behave as expected from the simplest, spherically averaged analytical models of structure formation, showing a negative correlation between their concentration and large-scale bias at all masses. In contrast, haloes in anisotropic, filament-like environments (αR ≳ 0.5) tend to show a positive correlation between bias and concentration at any mass. Our multiscale analysis cleanly demonstrates how the overall assembly bias trend across halo mass emerges as an average over these different halo populations, and provides valuable insights towards building analytical models that correctly incorporate assembly bias. We also discuss potential implications for the nature and detectability of galaxy assembly bias.

  10. The CogBIAS longitudinal study protocol: cognitive and genetic factors influencing psychological functioning in adolescence.

    Science.gov (United States)

    Booth, Charlotte; Songco, Annabel; Parsons, Sam; Heathcote, Lauren; Vincent, John; Keers, Robert; Fox, Elaine

    2017-12-29

    Optimal psychological development is dependent upon a complex interplay between individual and situational factors. Investigating the development of these factors in adolescence will help to improve understanding of emotional vulnerability and resilience. The CogBIAS longitudinal study (CogBIAS-L-S) aims to combine cognitive and genetic approaches to investigate risk and protective factors associated with the development of mood and impulsivity-related outcomes in an adolescent sample. CogBIAS-L-S is a three-wave longitudinal study of typically developing adolescents conducted over 4 years, with data collection at age 12, 14 and 16. At each wave participants will undergo multiple assessments including a range of selective cognitive processing tasks (e.g. attention bias, interpretation bias, memory bias) and psychological self-report measures (e.g. anxiety, depression, resilience). Saliva samples will also be collected at the baseline assessment for genetic analyses. Multilevel statistical analyses will be performed to investigate the developmental trajectory of cognitive biases on psychological functioning, as well as the influence of genetic moderation on these relationships. CogBIAS-L-S represents the first longitudinal study to assess multiple cognitive biases across adolescent development and the largest study of its kind to collect genetic data. It therefore provides a unique opportunity to understand how genes and the environment influence the development and maintenance of cognitive biases and provide insight into risk and protective factors that may be key targets for intervention.

  11. Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature.

    Directory of Open Access Journals (Sweden)

    Zhenglun Pan

    2005-12-01

    Full Text Available BACKGROUND: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. METHODS AND FINDINGS: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text. Many studies (14-35 per topic were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2-21 y after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001. The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se. Non-Chinese studies of Asian-descent populations (27% significant per se also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se. CONCLUSION: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.

  12. Comparison of stability statistics for yield in barley (Hordeum vulgare ...

    African Journals Online (AJOL)

    STORAGESEVER

    2010-03-15

    Mar 15, 2010 ... statistics and yield indicated that only TOP method would be useful for simultaneously selecting for high yield and ... metric stability methods; i) they reduce the bias caused by outliers, ii) ...... Biometrics, 43: 45-53. Sabaghnia N ...

  13. Empirical single sample quantification of bias and variance in Q-ball imaging.

    Science.gov (United States)

    Hainline, Allison E; Nath, Vishwesh; Parvathaneni, Prasanna; Blaber, Justin A; Schilling, Kurt G; Anderson, Adam W; Kang, Hakmook; Landman, Bennett A

    2018-02-06

    The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and variance of high angular resolution diffusion imaging metrics. The SIMEX approach is well established in the statistics literature and uses simulation of increasingly noisy data to extrapolate back to a hypothetical case with no noise. The bias of calculated metrics can then be computed by subtracting the SIMEX estimate from the original pointwise measurement. The SIMEX technique has been studied in the context of diffusion imaging to accurately capture the bias in fractional anisotropy measurements in DTI. Herein, we extend the application of SIMEX and bootstrap approaches to characterize bias and variance in metrics obtained from a Q-ball imaging reconstruction of high angular resolution diffusion imaging data. The results demonstrate that SIMEX and bootstrap approaches provide consistent estimates of the bias and variance of generalized fractional anisotropy, respectively. The RMSE for the generalized fractional anisotropy estimates shows a 7% decrease in white matter and an 8% decrease in gray matter when compared with the observed generalized fractional anisotropy estimates. On average, the bootstrap technique results in SD estimates that are approximately 97% of the true variation in white matter, and 86% in gray matter. Both SIMEX and bootstrap methods are flexible, estimate population characteristics based on single scans, and may be extended for bias and variance estimation on a variety of high angular resolution diffusion imaging metrics. © 2018 International Society for Magnetic Resonance in Medicine.

  14. Statistical theory and inference

    CERN Document Server

    Olive, David J

    2014-01-01

    This text is for  a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and uniformly most powerful  tests and the Neyman Pearson Lemma. A major goal of this text is to make these topics much more accessible to students by using the theory of exponential families. Exponential families, indicator functions and the support of the distribution are used throughout the text to simplify the theory. More than 50 ``brand name" distributions are used to illustrate the theory with many examples of exponential families, maximum likelihood estimators and uniformly minimum variance unbiased estimators. There are many homework problems with over 30 pages of solutions.

  15. Beyond attentional bias: a perceptual bias in a dot-probe task.

    Science.gov (United States)

    Bocanegra, Bruno R; Huijding, Jorg; Zeelenberg, René

    2012-12-01

    Previous dot-probe studies indicate that threat-related face cues induce a bias in spatial attention. Independently of spatial attention, a recent psychophysical study suggests that a bilateral fearful face cue improves low spatial-frequency perception (LSF) and impairs high spatial-frequency perception (HSF). Here, we combine these separate lines of research within a single dot-probe paradigm. We found that a bilateral fearful face cue, compared with a bilateral neutral face cue, speeded up responses to LSF targets and slowed down responses to HSF targets. This finding is important, as it shows that emotional cues in dot-probe tasks not only bias where information is preferentially processed (i.e., an attentional bias in spatial location), but also bias what type of information is preferentially processed (i.e., a perceptual bias in spatial frequency). PsycINFO Database Record (c) 2012 APA, all rights reserved.

  16. Market Efficiency and Behavioral Biases in the WNBA Betting Market

    Directory of Open Access Journals (Sweden)

    Rodney J. Paul

    2014-04-01

    Full Text Available The betting market for the Women’s National Basketball Association (WNBA is a thin financial market, which does not attract much interest from sports bettors. Given these characteristics, it is possible that profitable wagering strategies could exist for informed bettors of the WNBA. Using betting data on the WNBA from 2007–2012, we find that simple betting strategies do not earn statistically significant returns. WNBA bettors are like NBA bettors; however, in that they strongly prefer the best teams, particularly when they are on the road. Despite this clear bias, betting against the most popular public wagers is not found to earn statistically significant profits.

  17. Long-branch attraction bias and inconsistency in Bayesian phylogenetics.

    Directory of Open Access Journals (Sweden)

    Bryan Kolaczkowski

    Full Text Available Bayesian inference (BI of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML, so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias--which is apparent under both controlled simulation conditions and in analyses of empirical sequence data--also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages--that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis.

  18. APPLIED BEHAVIORAL FINANCE: INVESTOR BIASES, PERFORMANCE REVERSION TO THE MEAN and TREND FORMATION

    Directory of Open Access Journals (Sweden)

    ADRIAN MITROI

    2014-02-01

    Full Text Available In the pursuit of understanding the behavior of the market player, the basic argument relays on the supposition that the risk appetite increases exactly at the worst moment - when the capacity to assume additional risk decreases significantly.People view a sample randomly drawn from a population as highly representative and cvasi similar to the population in all its essential characteristics. They expect any two samples drawn from a particular population to be more similar to one another and to the population than is statistically justifiable. This behavior is different from the tenets of classic finance theory. The paper aims at demonstating that investor psychological biases lead to investment performance to tilt to the mean in the long run and by following the trend, the financial market population do not enjoy significant sustainable benefits. As a reflection of the behavioral biases and influences, the statistical demonstration supports the conclusion that markets do not random walk.

  19. Media bias under direct and indirect government control: when is the bias smaller?

    OpenAIRE

    Abhra Roy

    2015-01-01

    We present an analytical framework to compare media bias under direct and indirect government control. In this context, we show that direct control can lead to a smaller bias and higher welfare than indirect control. We further show that the size of the advertising market affects media bias only under direct control. Media bias, under indirect control, is not affected by the size of the advertising market.

  20. Addressing criticisms of existing predictive bias research: cognitive ability test scores still overpredict African Americans' job performance.

    Science.gov (United States)

    Berry, Christopher M; Zhao, Peng

    2015-01-01

    Predictive bias studies have generally suggested that cognitive ability test scores overpredict job performance of African Americans, meaning these tests are not predictively biased against African Americans. However, at least 2 issues call into question existing over-/underprediction evidence: (a) a bias identified by Aguinis, Culpepper, and Pierce (2010) in the intercept test typically used to assess over-/underprediction and (b) a focus on the level of observed validity instead of operational validity. The present study developed and utilized a method of assessing over-/underprediction that draws on the math of subgroup regression intercept differences, does not rely on the biased intercept test, allows for analysis at the level of operational validity, and can use meta-analytic estimates as input values. Therefore, existing meta-analytic estimates of key parameters, corrected for relevant statistical artifacts, were used to determine whether African American job performance remains overpredicted at the level of operational validity. African American job performance was typically overpredicted by cognitive ability tests across levels of job complexity and across conditions wherein African American and White regression slopes did and did not differ. Because the present study does not rely on the biased intercept test and because appropriate statistical artifact corrections were carried out, the present study's results are not affected by the 2 issues mentioned above. The present study represents strong evidence that cognitive ability tests generally overpredict job performance of African Americans. (c) 2015 APA, all rights reserved.

  1. Interpreting trial results following use of different intention-to-treat approaches for preventing attrition bias: a meta-epidemiological study protocol

    OpenAIRE

    Dossing, Anna; Tarp, Simon; Furst, Daniel E; Gluud, Christian; Beyene, Joseph; Hansen, Bjarke B; Bliddal, Henning; Christensen, Robin

    2014-01-01

    Introduction When participants drop out of randomised clinical trials, as frequently happens, the intention-to-treat (ITT) principle does not apply, potentially leading to attrition bias. Data lost from patient dropout/lack of follow-up are statistically addressed by imputing, a procedure prone to bias. Deviations from the original definition of ITT are referred to as modified intention-to-treat (mITT). As yet, the impact of the potential bias associated with mITT has not been assessed. Our o...

  2. The familiar versus the unfamiliar: Familiarity bias amongst individual investors

    Directory of Open Access Journals (Sweden)

    Annalien De Vries

    2017-02-01

    Full Text Available Purpose: The purpose of this study was to investigate the existence of familiarity bias amongst individual investors in the South African stock market. Problem investigated: According to Warren Buffet, one needs to maintain emotional detachment if one wants to be a successful investor. However, recent research indicates that the perceptions of companies’ products and brands may influence individuals’ investment decisions in the stock market. This phenomenon implies that the investment decisions of individual investors are not purely based on firm fundamentals as suggested by traditional finance theories, but might be driven partly by the positive or negative attitude they have towards certain companies’ products and brands. The existence of familiarity bias amongst individual investors was investigated to determine if individuals prefer to invest in companies they are familiar with as opposed to unfamiliar companies. Methodology: A quantitative approach was followed. An online survey was used to show images of familiar and unfamiliar company brands to respondents, whereafter respondents were asked to indicate whether they will invest in the shares of the identified companies. The statistical analysis entailed descriptive statistics as well as one-way analyses of variance to test the stated hypotheses. Main findings: The results of this exploratory study indicate that investors do exhibit familiarity bias when choosing between different companies to invest in. Value of the research: The inclination of individual investors to invest in familiar corporate brands can have implications for the marketing industry, financial markets, the performance of companies as well as the investment performance of individual investors in the sense that it would seem that company brands could have an influence on investment decisions.

  3. Information environment, behavioral biases, and home bias in analysts’ recommendations

    DEFF Research Database (Denmark)

    Farooq, Omar; Taouss, Mohammed

    2012-01-01

    Can information environment of a firm explain home bias in analysts’ recommendations? Can the extent of agency problems explain optimism difference between foreign and local analysts? This paper answers these questions by documenting the effect of information environment on home bias in analysts’...

  4. Cognitive advantage in bilingualism: an example of publication bias?

    Science.gov (United States)

    de Bruin, Angela; Treccani, Barbara; Della Sala, Sergio

    2015-01-01

    It is a widely held belief that bilinguals have an advantage over monolinguals in executive-control tasks, but is this what all studies actually demonstrate? The idea of a bilingual advantage may result from a publication bias favoring studies with positive results over studies with null or negative effects. To test this hypothesis, we looked at conference abstracts from 1999 to 2012 on the topic of bilingualism and executive control. We then determined which of the studies they reported were subsequently published. Studies with results fully supporting the bilingual-advantage theory were most likely to be published, followed by studies with mixed results. Studies challenging the bilingual advantage were published the least. This discrepancy was not due to differences in sample size, tests used, or statistical power. A test for funnel-plot asymmetry provided further evidence for the existence of a publication bias. © The Author(s) 2014.

  5. Biasing secondary particle interaction physics and production in MCNP6

    International Nuclear Information System (INIS)

    Fensin, M.L.; James, M.R.

    2016-01-01

    Highlights: • Biasing secondary production and interactions of charged particles in the tabular energy regime. • Examining lower weight window bounds for rare events when using Russian roulette. • The new biasing strategy can speedup calculations by a factor of 1 million or more. - Abstract: Though MCNP6 will transport elementary charged particles and light ions to low energies (i.e. less than 20 MeV), MCNP6 has historically relied on model physics with suggested minimum energies of ∼20 to 200 MeV. Use of library data for the low energy regime was developed for MCNP6 1.1.Beta to read and use light ion libraries. Thick target yields of neutron production for alphas on fluoride result in 1 production event per roughly million sampled alphas depending on the energy of the alpha (for other isotopes the yield can be even rarer). Calculation times to achieve statistically significant and converged thick target yields are quite laborious, needing over one hundred processor hours. The MUCEND code possess a biasing technique for improving the sampling of secondary particle production by forcing a nuclear interaction to occur per each alpha transported. We present here a different biasing strategy for secondary particle production from charged particles. During each substep, as the charged particle slows down, we bias both a nuclear collision event to occur at each substep and the production of secondary particles at the collision event, while still continuing to progress the charged particle until reaching a region of zero importance or an energy/time cutoff. This biasing strategy is capable of speeding up calculations by a factor of a million or more as compared to the unbiased calculation. Further presented here are both proof that the biasing strategy is capable of producing the same results as the unbiased calculation and the limitations to consider in order to achieve accurate results of secondary particle production. Though this strategy was developed for MCNP

  6. Statistical techniques to extract information during SMAP soil moisture assimilation

    Science.gov (United States)

    Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.

    2017-12-01

    Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

  7. Selection bias in studies of human reproduction-longevity trade-offs.

    Science.gov (United States)

    Helle, Samuli

    2017-12-13

    A shorter lifespan as a potential cost of high reproductive effort in humans has intrigued researchers for more than a century. However, the results have been inconclusive so far and despite strong theoretical expectations we do not currently have compelling evidence for the longevity costs of reproduction. Using Monte Carlo simulation, it is shown here that a common practice in human reproduction-longevity studies using historical data (the most relevant data sources for this question), the omission of women who died prior to menopausal age from the analysis, results in severe underestimation of the potential underlying trade-off between reproduction and lifespan. In other words, assuming that such a trade-off is expressed also during reproductive years, the strength of the trade-off between reproduction and lifespan is progressively weakened when women dying during reproductive ages are sequentially and non-randomly excluded from the analysis. In cases of small sample sizes (e.g. few hundreds of observations), this selection bias by reducing statistical power may even partly explain the null results commonly found in this field. Future studies in this field should thus apply statistical approaches that account for or avoid selection bias in order to recover reliable effect size estimates between reproduction and longevity. © 2017 The Author(s).

  8. Can Family Planning Service Statistics Be Used to Track Population-Level Outcomes?

    Science.gov (United States)

    Magnani, Robert J; Ross, John; Williamson, Jessica; Weinberger, Michelle

    2018-03-21

    The need for annual family planning program tracking data under the Family Planning 2020 (FP2020) initiative has contributed to renewed interest in family planning service statistics as a potential data source for annual estimates of the modern contraceptive prevalence rate (mCPR). We sought to assess (1) how well a set of commonly recorded data elements in routine service statistics systems could, with some fairly simple adjustments, track key population-level outcome indicators, and (2) whether some data elements performed better than others. We used data from 22 countries in Africa and Asia to analyze 3 data elements collected from service statistics: (1) number of contraceptive commodities distributed to clients, (2) number of family planning service visits, and (3) number of current contraceptive users. Data quality was assessed via analysis of mean square errors, using the United Nations Population Division World Contraceptive Use annual mCPR estimates as the "gold standard." We also examined the magnitude of several components of measurement error: (1) variance, (2) level bias, and (3) slope (or trend) bias. Our results indicate modest levels of tracking error for data on commodities to clients (7%) and service visits (10%), and somewhat higher error rates for data on current users (19%). Variance and slope bias were relatively small for all data elements. Level bias was by far the largest contributor to tracking error. Paired comparisons of data elements in countries that collected at least 2 of the 3 data elements indicated a modest advantage of data on commodities to clients. None of the data elements considered was sufficiently accurate to be used to produce reliable stand-alone annual estimates of mCPR. However, the relatively low levels of variance and slope bias indicate that trends calculated from these 3 data elements can be productively used in conjunction with the Family Planning Estimation Tool (FPET) currently used to produce annual m

  9. Bias-limited extraction of cosmological parameters

    Energy Technology Data Exchange (ETDEWEB)

    Shimon, Meir; Itzhaki, Nissan; Rephaeli, Yoel, E-mail: meirs@wise.tau.ac.il, E-mail: nitzhaki@post.tau.ac.il, E-mail: yoelr@wise.tau.ac.il [School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978 (Israel)

    2013-03-01

    It is known that modeling uncertainties and astrophysical foregrounds can potentially introduce appreciable bias in the deduced values of cosmological parameters. While it is commonly assumed that these uncertainties will be accounted for to a sufficient level of precision, the level of bias has not been properly quantified in most cases of interest. We show that the requirement that the bias in derived values of cosmological parameters does not surpass nominal statistical error, translates into a maximal level of overall error O(N{sup −½}) on |ΔP(k)|/P(k) and |ΔC{sub l}|/C{sub l}, where P(k), C{sub l}, and N are the matter power spectrum, angular power spectrum, and number of (independent Fourier) modes at a given scale l or k probed by the cosmological survey, respectively. This required level has important consequences on the precision with which cosmological parameters are hoped to be determined by future surveys: in virtually all ongoing and near future surveys N typically falls in the range 10{sup 6}−10{sup 9}, implying that the required overall theoretical modeling and numerical precision is already very high. Future redshifted-21-cm observations, projected to sample ∼ 10{sup 14} modes, will require knowledge of the matter power spectrum to a fantastic 10{sup −7} precision level. We conclude that realizing the expected potential of future cosmological surveys, which aim at detecting 10{sup 6}−10{sup 14} modes, sets the formidable challenge of reducing the overall level of uncertainty to 10{sup −3}−10{sup −7}.

  10. Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

    Energy Technology Data Exchange (ETDEWEB)

    Callister, Stephen J.; Barry, Richard C.; Adkins, Joshua N.; Johnson, Ethan T.; Qian, Weijun; Webb-Robertson, Bobbie-Jo M.; Smith, Richard D.; Lipton, Mary S.

    2006-02-01

    Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias, assigned ranks among the techniques revealed significant trends. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that this technique was more generally suitable for reducing systematic biases.

  11. Effect of Belief Bias on the Development of Undergraduate Students' Reasoning about Inference

    Science.gov (United States)

    Kaplan, Jennifer K.

    2009-01-01

    Psychologists have discovered a phenomenon called "Belief Bias" in which subjects rate the strength of arguments based on the believability of the conclusions. This paper reports the results of a small qualitative pilot study of undergraduate students who had previously taken an algebra-based introduction to statistics class. The subjects in this…

  12. Bond and Equity Home Bias and Foreign Bias: an International Study

    OpenAIRE

    VanPée, Rosanne; De Moor, Lieven

    2012-01-01

    In this paper we explore tentatively and formally the differences between bond and equity home bias and foreign bias based on one large scale dataset including developed and emerging markets for the period 2001 to 2010. We set the stage by tentatively and formally linking the diversion of bond and equity home bias in OECD countries to the increasing public debt issues under the form of government bonds i.e. the supply-driven argument. Unlike Fidora et al. (2007) we do not find that exchange r...

  13. Full counting statistics of multiple Andreev reflections in incoherent diffusive superconducting junctions

    International Nuclear Information System (INIS)

    Samuelsson, P.

    2007-01-01

    We present a theory for the full distribution of current fluctuations in incoherent diffusive superconducting junctions, subjected to a voltage bias. This theory of full counting statistics of incoherent multiple Andreev reflections is valid for an arbitrary applied voltage. We present a detailed discussion of the properties of the first four cumulants as well as the low and high voltage regimes of the full counting statistics. (orig.)

  14. Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy.

    Directory of Open Access Journals (Sweden)

    Kieran A Monaghan

    Full Text Available Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data

  15. Subclinical delusional ideation and appreciation of sample size and heterogeneity in statistical judgment.

    Science.gov (United States)

    Galbraith, Niall D; Manktelow, Ken I; Morris, Neil G

    2010-11-01

    Previous studies demonstrate that people high in delusional ideation exhibit a data-gathering bias on inductive reasoning tasks. The current study set out to investigate the factors that may underpin such a bias by examining healthy individuals, classified as either high or low scorers on the Peters et al. Delusions Inventory (PDI). More specifically, whether high PDI scorers have a relatively poor appreciation of sample size and heterogeneity when making statistical judgments. In Expt 1, high PDI scorers made higher probability estimates when generalizing from a sample of 1 with regard to the heterogeneous human property of obesity. In Expt 2, this effect was replicated and was also observed in relation to the heterogeneous property of aggression. The findings suggest that delusion-prone individuals are less appreciative of the importance of sample size when making statistical judgments about heterogeneous properties; this may underpin the data gathering bias observed in previous studies. There was some support for the hypothesis that threatening material would exacerbate high PDI scorers' indifference to sample size.

  16. Statistics of peaks in cosmological nonlinear density fields

    International Nuclear Information System (INIS)

    Suginohara, Tatsushi; Suto, Yasushi.

    1990-06-01

    Distribution of the high-density peaks in the universe is examined using N-body simulations. Nonlinear evolution of the underlying density field significantly changes the statistical properties of the peaks, compared with the analytic results valid for the random Gaussian field. In particular, the abundances and correlations of the initial density peaks are discussed in the context of biased galaxy formation theory. (author)

  17. Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study

    Directory of Open Access Journals (Sweden)

    Jane Candlish

    2017-01-01

    Full Text Available Abstract Background The cohort multiple randomised controlled trial (cmRCT design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to address a key concern of the cmRCT design: refusal to treatment is only present in the intervention arm, and this may lead to bias and reduce statistical power. Methods We used simulation studies to assess the effect of this refusal, both random and related to event risk, on bias of the effect estimator and statistical power. A series of simulations were undertaken that represent a cmRCT trial with time-to-event endpoint. Intention-to-treat (ITT, per protocol (PP, and instrumental variable (IV analysis methods, two stage predictor substitution and two stage residual inclusion, were compared for various refusal scenarios. Results We found the IV methods provide a less biased estimator for the causal effect when refusal is present in the intervention arm, with the two stage residual inclusion method performing best with regards to minimum bias and sufficient power. We demonstrate that sample sizes should be adapted based on expected and actual refusal rates in order to be sufficiently powered for IV analysis. Conclusion We recommend running both an IV and ITT analyses in an individually randomised cmRCT as it is expected that the effect size of interest, or the effect we would observe in clinical practice, would lie somewhere between that estimated with ITT and IV analyses. The optimum (in terms of bias and power instrumental variable method was the two stage residual inclusion method. We recommend using adaptive power calculations, updating them as refusal rates are collected in the trial recruitment phase in order to be sufficiently powered for IV analysis.

  18. Exploring Selective Exposure and Confirmation Bias as Processes Underlying Employee Work Happiness: An Intervention Study.

    Science.gov (United States)

    Williams, Paige; Kern, Margaret L; Waters, Lea

    2016-01-01

    Employee psychological capital (PsyCap), perceptions of organizational virtue (OV), and work happiness have been shown to be associated within and over time. This study examines selective exposure and confirmation bias as potential processes underlying PsyCap, OV, and work happiness associations. As part of a quasi-experimental study design, school staff (N = 69) completed surveys at three time points. After the first assessment, some staff (n = 51) completed a positive psychology training intervention. Results of descriptive statistics, correlation, and regression analyses on the intervention group provide some support for selective exposure and confirmation bias as explanatory mechanisms. In focusing on the processes through which employee attitudes may influence work happiness this study advances theoretical understanding, specifically of selective exposure and confirmation bias in a field study context.

  19. Are Managed Futures Indices Telling Truth? Biases in CTA Databases and Proposals of Potential Enhancements

    Directory of Open Access Journals (Sweden)

    Adam Zaremba

    2011-07-01

    Full Text Available Managed futures are an alternative asset class which has recently became considerably popular among investment industry. However, due to its characteristics, access to managed futures historical performance statistics is relatively confined. All available information originates from commercial and academic databases, reporting to which is entirely voluntary. This situation results in series of biases which distort the managed futures performance in the eyes of investors. The paper consists of two parts. First, the author reviews and describes various biases that influence the reliability of the managed futures indices and databases. The second section encompasses author’s proposals of potential enhancements, which aim to reduce the impact of the biases in order to derive a benchmark that could better reflect characteristics of managed futures investment from the point of view of a potential investor.

  20. Cognitive Bias in Systems Verification

    Science.gov (United States)

    Larson, Steve

    2012-01-01

    Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

  1. Biases in categorization

    NARCIS (Netherlands)

    Das-Smaal, E.A.

    1990-01-01

    On what grounds can we conclude that an act of categorization is biased? In this chapter, it is contended that in the absence of objective norms of what categories actually are, biases in categorization can only be specified in relation to theoretical understandings of categorization. Therefore, the

  2. Cognitive bias measurement and social anxiety disorder: Correlating self-report data and attentional bias

    Directory of Open Access Journals (Sweden)

    Alexander Miloff

    2015-09-01

    Full Text Available Social anxiety disorder (SAD and attentional bias are theoretically connected in cognitive behavioral therapeutic models. In fact, there is an emerging field focusing on modifying attentional bias as a stand-alone treatment. However, it is unclear to what degree these attentional biases are present before commencing treatment. The purpose of this study was to measure pre-treatment attentional bias in 153 participants diagnosed with SAD using a home-based Internet version of the dot-probe paradigm. Results showed no significant correlation for attentional bias (towards or away from negative words or faces and the self-rated version of the Liebowitz Social Anxiety Scale (LSAS-SR. However, two positive correlations were found for the secondary measures Generalized Anxiety Disorder 7 (GAD-7 and Patient Health Questionnaire 9 (PHQ-9. These indicated that those with elevated levels of anxiety and depression had a higher bias towards negative faces in neutral–negative and positive–negative valence combinations, respectively. The unreliability of the dot-probe paradigm and home-based Internet delivery are discussed to explain the lack of correlations between LSAS-SR and attentional bias. Changes to the dot-probe task are suggested that could improve reliability.

  3. Publication bias was not a good reason to discourage trials with low power.

    Science.gov (United States)

    Borm, George F; den Heijer, Martin; Zielhuis, Gerhard A

    2009-01-01

    The objective was to investigate whether it is justified to discourage trials with less than 80% power. Trials with low power are unlikely to produce conclusive results, but their findings can be used by pooling then in a meta-analysis. However, such an analysis may be biased, because trials with low power are likely to have a nonsignificant result and are less likely to be published than trials with a statistically significant outcome. We simulated several series of studies with varying degrees of publication bias and then calculated the "real" one-sided type I error and the bias of meta-analyses with a "nominal" error rate (significance level) of 2.5%. In single trials, in which heterogeneity was set at zero, low, and high, the error rates were 2.3%, 4.7%, and 16.5%, respectively. In multiple trials with 80%-90% power and a publication rate of 90% when the results were nonsignificant, the error rates could be as high as 5.1%. When the power was 50% and the publication rate of non-significant results was 60%, the error rates did not exceed 5.3%, whereas the bias was at most 15% of the difference used in the power calculation. The impact of publication bias does not warrant the exclusion of trials with 50% power.

  4. MASKED AREAS IN SHEAR PEAK STATISTICS: A FORWARD MODELING APPROACH

    International Nuclear Information System (INIS)

    Bard, D.; Kratochvil, J. M.; Dawson, W.

    2016-01-01

    The statistics of shear peaks have been shown to provide valuable cosmological information beyond the power spectrum, and will be an important constraint of models of cosmology in forthcoming astronomical surveys. Surveys include masked areas due to bright stars, bad pixels etc., which must be accounted for in producing constraints on cosmology from shear maps. We advocate a forward-modeling approach, where the impacts of masking and other survey artifacts are accounted for in the theoretical prediction of cosmological parameters, rather than correcting survey data to remove them. We use masks based on the Deep Lens Survey, and explore the impact of up to 37% of the survey area being masked on LSST and DES-scale surveys. By reconstructing maps of aperture mass the masking effect is smoothed out, resulting in up to 14% smaller statistical uncertainties compared to simply reducing the survey area by the masked area. We show that, even in the presence of large survey masks, the bias in cosmological parameter estimation produced in the forward-modeling process is ≈1%, dominated by bias caused by limited simulation volume. We also explore how this potential bias scales with survey area and evaluate how much small survey areas are impacted by the differences in cosmological structure in the data and simulated volumes, due to cosmic variance

  5. Publication selection and the income elasticity of the value of a statistical life.

    Science.gov (United States)

    Doucouliagos, Hristos; Stanley, T D; Viscusi, W Kip

    2014-01-01

    Estimates of the value of a statistical life (VSL) establish the price government agencies use to value fatality risks. Transferring these valuations to other populations often utilizes the income elasticity of the VSL, which typically draw on estimates from meta-analyses. Using a data set consisting of 101 estimates of the income elasticity of VSL from 14 previously reported meta-analyses, we find that after accounting for potential publication bias the income elasticity of value of a statistical life is clearly and robustly inelastic, with a value of approximately 0.25-0.63. There is also clear evidence of the importance of controlling for levels of risk, differential publication selection bias, and the greater income sensitivity of VSL from stated preference surveys. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. Optimistic Bias, Risk Factors, and Development of High Blood Pressure and Obesity among African American Adolescents in Mississippi (USA

    Directory of Open Access Journals (Sweden)

    Monique S. White

    2017-02-01

    Full Text Available Childhood obesity has reached epidemic proportions and is linked to hypertension among African American youth. Optimistic bias influences behavior of youth causing them to underestimate their susceptibility to negative health outcomes. This study explored adolescent behaviors and prevalence of high blood pressure and obesity in a school district. We examined the relationship between individual health risk practices and optimistic bias on health outcomes; 433 African American high school students were administered a survey and had their obesity and blood pressure measured by the school nurse. Canonical correlational analyses were used to examine relationships between health risk practices and descriptive statistics for optimistic bias and health outcomes. Engaging in moderate exercise for at least 30 min in the last 7 days and lower blood pressure was the only statistically significant relationship. Two-thirds of the students did not perceive themselves to be at risk of developing cardiovascular disease with males at greater risk than females, despite the presence of clinical risk factors for hypertension and obesity. Reducing health optimistic bias is an effective way of motivating young people to adopt more positive behaviors using educational institutions to implement intervention programs that promote positive health behavior as a way to reduce health disparities.

  7. Threat bias, not negativity bias, underpins differences in political ideology.

    Science.gov (United States)

    Lilienfeld, Scott O; Latzman, Robert D

    2014-06-01

    Although disparities in political ideology are rooted partly in dispositional differences, Hibbing et al.'s analysis paints with an overly broad brush. Research on the personality correlates of liberal-conservative differences points not to global differences in negativity bias, but to differences in threat bias, probably emanating from differences in fearfulness. This distinction bears implications for etiological research and persuasion efforts.

  8. Statistical Discrimination in a Competitive Labor Market

    OpenAIRE

    Jonathan B. Berk

    1999-01-01

    This paper studies the effect of employee job selection in a model of statistical discrimination in a competitive labor market. In an economy in which there are quality differences between groups, a surprisingly strong condition is required to guarantee discrimination against the worse qualified group --- MLRP must hold. In addition, because of the self-selection bias induced by competition, the resulting discrimination is small when compared to the magnitude of the underlying quality differe...

  9. Can the variability in precipitation simulations across GCMs be reduced through sensible bias correction?

    Science.gov (United States)

    Nguyen, Ha; Mehrotra, Rajeshwar; Sharma, Ashish

    2017-11-01

    This work investigates the performance of four bias correction alternatives for representing persistence characteristics of precipitation across 37 General Circulation Models (GCMs) from the CMIP5 data archive. The first three correction approaches are the Simple Monthly Bias Correction (SMBC), Equidistance Quantile Mapping (EQM), and Nested Bias Correction (NBC), all of which operate in the time domain, with a focus on representing distributional and moment attributes in the observed precipitation record. The fourth approach corrects for the biases in high- and low-frequency variability or persistence of the GCM time series in the frequency domain and is named as Frequency-based Bias Correction (FBC). The Climatic Research Unit (CRU) gridded precipitation data covering the global land surface is used as a reference dataset. The assessment focusses on current and future means, variability, and drought-related characteristics at different temporal and spatial scales. For the current climate, all bias correction approaches perform reasonably well at the global scale by reproducing the observed precipitation statistics. For the future climate, focus is drawn on the agreement of the attributes across the GCMs considered. The inter-model difference/spread of each attribute across the GCMs is used as a measure of this agreement. Our results indicate that out of the four bias correction approaches used, FBC provides the lowest inter-model spreads, specifically for persistence attributes, over most regions/ parts over the global land surface. This has significant implications for most hydrological studies where the effect of low-frequency variability is of considerable importance.

  10. Statistical and extra-statistical considerations in differential item functioning analyses

    Directory of Open Access Journals (Sweden)

    G. K. Huysamen

    2004-10-01

    Full Text Available This article briefly describes the main procedures for performing differential item functioning (DIF analyses and points out some of the statistical and extra-statistical implications of these methods. Research findings on the sources of DIF, including those associated with translated tests, are reviewed. As DIF analyses are oblivious of correlations between a test and relevant criteria, the elimination of differentially functioning items does not necessarily improve predictive validity or reduce any predictive bias. The implications of the results of past DIF research for test development in the multilingual and multi-cultural South African society are considered. Opsomming Hierdie artikel beskryf kortliks die hoofprosedures vir die ontleding van differensiële itemfunksionering (DIF en verwys na sommige van die statistiese en buite-statistiese implikasies van hierdie metodes. ’n Oorsig word verskaf van navorsingsbevindings oor die bronne van DIF, insluitend dié by vertaalde toetse. Omdat DIF-ontledings nie die korrelasies tussen ’n toets en relevante kriteria in ag neem nie, sal die verwydering van differensieel-funksionerende items nie noodwendig voorspellingsgeldigheid verbeter of voorspellingsydigheid verminder nie. Die implikasies van vorige DIF-navorsingsbevindings vir toetsontwikkeling in die veeltalige en multikulturele Suid-Afrikaanse gemeenskap word oorweeg.

  11. Good practices for quantitative bias analysis.

    Science.gov (United States)

    Lash, Timothy L; Fox, Matthew P; MacLehose, Richard F; Maldonado, George; McCandless, Lawrence C; Greenland, Sander

    2014-12-01

    Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them. Finally, by suggesting aspects that dominate uncertainty in a particular research result or topic area, bias analysis can guide efficient allocation of sparse research resources. The fundamental methods of bias analyses have been known for decades, and there have been calls for more widespread use for nearly as long. There was a time when some believed that bias analyses were rarely undertaken because the methods were not widely known and because automated computing tools were not readily available to implement the methods. These shortcomings have been largely resolved. We must, therefore, contemplate other barriers to implementation. One possibility is that practitioners avoid the analyses because they lack confidence in the practice of bias analysis. The purpose of this paper is therefore to describe what we view as good practices for applying quantitative bias analysis to epidemiological data, directed towards those familiar with the methods. We focus on answering questions often posed to those of us who advocate incorporation of bias analysis methods into teaching and research. These include the following. When is bias analysis practical and productive? How does one select the biases that ought to be addressed? How does one select a method to model biases? How does one assign values to the parameters of a bias model? How does one present and interpret a bias analysis?. We hope that our guide to good practices for conducting and presenting bias analyses will encourage

  12. Hindsight bias and outcome bias in the social construction of medical negligence: a review.

    Science.gov (United States)

    Hugh, Thomas B; Dekker, Sidney W A

    2009-05-01

    Medical negligence has been the subject of much public debate in recent decades. Although the steep increase in the frequency and size of claims against doctors at the end of the last century appears to have plateaued, in Australia at least, medical indemnity costs and consequences are still a matter of concern for doctors, medical defence organisations and governments in most developed countries. Imprecision in the legal definition of negligence opens the possibility that judgments of this issue at several levels may be subject to hindsight and outcome bias. Hindsight bias relates to the probability of an adverse event perceived by a retrospective observer ("I would have known it was going to happen"), while outcome bias is a largely subconscious cognitive distortion produced by the observer's knowledge of the adverse outcome. This review examines the relevant legal, medical, psychological and sociological literature on the operation of these pervasive and universal biases in the retrospective evaluation of adverse events. A finding of medical negligence is essentially an after-the-event social construction and is invariably affected by hindsight bias and knowledge of the adverse outcome. Such biases obviously pose a threat to the fairness of judgments. A number of debiasing strategies have been suggested but are relatively ineffective because of the universality and strength of these biases and the inherent difficulty of concealing from expert witnesses knowledge of the outcome. Education about the effect of the biases is therefore important for lawyers, medical expert witnesses and the judiciary.

  13. Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

    Directory of Open Access Journals (Sweden)

    Martin M Monti

    2011-03-01

    Full Text Available Functional Magnetic Resonance Imaging (fMRI is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a General Linear Model (GLM approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

  14. Photovoltaic Bias Generator

    Science.gov (United States)

    2018-02-01

    Department of the Army position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does not constitute an... Interior view of the photovoltaic bias generator showing wrapped-wire side of circuit board...3 Fig. 4 Interior view of the photovoltaic bias generator showing component side of circuit board

  15. Bias-correction in vector autoregressive models

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    2014-01-01

    We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...

  16. Sampling bias in an internet treatment trial for depression.

    Science.gov (United States)

    Donkin, L; Hickie, I B; Christensen, H; Naismith, S L; Neal, B; Cockayne, N L; Glozier, N

    2012-10-23

    Internet psychological interventions are efficacious and may reduce traditional access barriers. No studies have evaluated whether any sampling bias exists in these trials that may limit the translation of the results of these trials into real-world application. We identified 7999 potentially eligible trial participants from a community-based health cohort study and invited them to participate in a randomized controlled trial of an online cognitive behavioural therapy programme for people with depression. We compared those who consented to being assessed for trial inclusion with nonconsenters on demographic, clinical and behavioural indicators captured in the health study. Any potentially biasing factors were then assessed for their association with depression outcome among trial participants to evaluate the existence of sampling bias. Of the 35 health survey variables explored, only 4 were independently associated with higher likelihood of consenting-female sex (odds ratio (OR) 1.11, 95% confidence interval (CI) 1.05-1.19), speaking English at home (OR 1.48, 95% CI 1.15-1.90) higher education (OR 1.67, 95% CI 1.46-1.92) and a prior diagnosis of depression (OR 1.37, 95% CI 1.22-1.55). The multivariate model accounted for limited variance (C-statistic 0.6) in explaining participation. These four factors were not significantly associated with either the primary trial outcome measure or any differential impact by intervention arm. This demonstrates that, among eligible trial participants, few factors were associated with the consent to participate. There was no indication that such self-selection biased the trial results or would limit the generalizability and translation into a public or clinical setting.

  17. Bias versus bias: harnessing hindsight to reveal paranormal belief change beyond demand characteristics.

    Science.gov (United States)

    Kane, Michael J; Core, Tammy J; Hunt, R Reed

    2010-04-01

    Psychological change is difficult to assess, in part because self-reported beliefs and attitudes may be biased or distorted. The present study probed belief change, in an educational context, by using the hindsight bias to counter another bias that generally plagues assessment of subjective change. Although research has indicated that skepticism courses reduce paranormal beliefs, those findings may reflect demand characteristics (biases toward desired, skeptical responses). Our hindsight-bias procedure circumvented demand by asking students, following semester-long skepticism (and control) courses, to recall their precourse levels of paranormal belief. People typically remember themselves as previously thinking, believing, and acting as they do now, so current skepticism should provoke false recollections of previous skepticism. Given true belief change, therefore, skepticism students should have remembered themselves as having been more skeptical than they were. They did, at least about paranormal topics that were covered most extensively in the course. Our findings thus show hindsight to be useful in evaluating cognitive change beyond demand characteristics.

  18. Standard guide for preparing and interpreting precision and bias statements in test method standards used in the nuclear industry

    CERN Document Server

    American Society for Testing and Materials. Philadelphia

    1992-01-01

    1.1 This guide covers terminology useful for the preparation and interpretation of precision and bias statements. 1.2 In formulating precision and bias statements, it is important to understand the statistical concepts involved and to identify the major sources of variation that affect results. Appendix X1 provides a brief summary of these concepts. 1.3 To illustrate the statistical concepts and to demonstrate some sources of variation, a hypothetical data set has been analyzed in Appendix X2. Reference to this example is made throughout this guide. 1.4 It is difficult and at times impossible to ship nuclear materials for interlaboratory testing. Thus, precision statements for test methods relating to nuclear materials will ordinarily reflect only within-laboratory variation.

  19. Birth order and sibship size: evaluation of the role of selection bias in a case-control study of non-Hodgkin's lymphoma.

    Science.gov (United States)

    Mensah, F K; Willett, E V; Simpson, J; Smith, A G; Roman, E

    2007-09-15

    Substantial heterogeneity has been observed among case-control studies investigating associations between non-Hodgkin's lymphoma and familial characteristics, such as birth order and sibship size. The potential role of selection bias in explaining such heterogeneity is considered within this study. Selection bias according to familial characteristics and socioeconomic status is investigated within a United Kingdom-based case-control study of non-Hodgkin's lymphoma diagnosed during 1998-2001. Reported distributions of birth order and maternal age are each compared with expected reference distributions derived using national birth statistics from the United Kingdom. A method is detailed in which yearly data are used to derive expected distributions, taking account of variability in birth statistics over time. Census data are used to reweight both the case and control study populations such that they are comparable with the general population with regard to socioeconomic status. The authors found little support for an association between non-Hodgkin's lymphoma and birth order or family size and little evidence for an influence of selection bias. However, the findings suggest that between-study heterogeneity could be explained by selection biases that influence the demographic characteristics of participants.

  20. Inference on network statistics by restricting to the network space: applications to sexual history data.

    Science.gov (United States)

    Goyal, Ravi; De Gruttola, Victor

    2018-01-30

    Analysis of sexual history data intended to describe sexual networks presents many challenges arising from the fact that most surveys collect information on only a very small fraction of the population of interest. In addition, partners are rarely identified and responses are subject to reporting biases. Typically, each network statistic of interest, such as mean number of sexual partners for men or women, is estimated independently of other network statistics. There is, however, a complex relationship among networks statistics; and knowledge of these relationships can aid in addressing concerns mentioned earlier. We develop a novel method that constrains a posterior predictive distribution of a collection of network statistics in order to leverage the relationships among network statistics in making inference about network properties of interest. The method ensures that inference on network properties is compatible with an actual network. Through extensive simulation studies, we also demonstrate that use of this method can improve estimates in settings where there is uncertainty that arises both from sampling and from systematic reporting bias compared with currently available approaches to estimation. To illustrate the method, we apply it to estimate network statistics using data from the Chicago Health and Social Life Survey. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  1. Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

    Directory of Open Access Journals (Sweden)

    David Herrera

    Full Text Available In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS. When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'. As an alternative, a 'Gellerman-like training schedule' (GLS can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide

  2. Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

    Science.gov (United States)

    Herrera, David; Treviño, Mario

    2015-01-01

    In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of

  3. Triangles in ROC space: History and theory of "nonparametric" measures of sensitivity and response bias.

    Science.gov (United States)

    Macmillan, N A; Creelman, C D

    1996-06-01

    Can accuracy and response bias in two-stimulus, two-response recognition or detection experiments be measured nonparametrically? Pollack and Norman (1964) answered this question affirmatively for sensitivity, Hodos (1970) for bias: Both proposed measures based on triangular areas in receiver-operating characteristic space. Their papers, and especially a paper by Grier (1971) that provided computing formulas for the measures, continue to be heavily cited in a wide range of content areas. In our sample of articles, most authors described triangle-based measures as making fewer assumptions than measures associated with detection theory. However, we show that statistics based on products or ratios of right triangle areas, including a recently proposed bias index and a not-yetproposed but apparently plausible sensitivity index, are consistent with a decision process based on logistic distributions. Even the Pollack and Norman measure, which is based on non-right triangles, is approximately logistic for low values of sensitivity. Simple geometric models for sensitivity and bias are not nonparametric, even if their implications are not acknowledged in the defining publications.

  4. Non-Gaussian Halo Bias Re-examined: Mass-dependent Amplitude from the Peak-Background Split and Thresholding

    International Nuclear Information System (INIS)

    Desjacques, Vincent; Jeong, Donghui; Schmidt, Fabian

    2011-01-01

    Recent results of N-body simulations have shown that current theoretical models are not able to correctly predict the amplitude of the scale-dependent halo bias induced by primordial non-Gaussianity, for models going beyond the simplest, local quadratic case. Motivated by these discrepancies, we carefully examine three theoretical approaches based on (1) the statistics of thresholded regions, (2) a peak-background split method based on separation of scales, and (3) a peak-background split method using the conditional mass function. We first demonstrate that the statistics of thresholded regions, which is shown to be equivalent at leading order to a local bias expansion, cannot explain the mass-dependent deviation between theory and N-body simulations. In the two formulations of the peak-background split on the other hand, we identify an important, but previously overlooked, correction to the non-Gaussian bias that strongly depends on halo mass. This new term is in general significant for any primordial non-Gaussianity going beyond the simplest local f NL model. In a separate paper (to be published in PRD rapid communication), the authors compare these new theoretical predictions with N-body simulations, showing good agreement for all simulated types of non-Gaussianity.

  5. Unifying quantum heat transfer in a nonequilibrium spin-boson model with full counting statistics

    Science.gov (United States)

    Wang, Chen; Ren, Jie; Cao, Jianshu

    2017-02-01

    To study the full counting statistics of quantum heat transfer in a driven nonequilibrium spin-boson model, we develop a generalized nonequilibrium polaron-transformed Redfield equation with an auxiliary counting field. This enables us to study the impact of qubit-bath coupling ranging from weak to strong regimes. Without external modulations, we observe maximal values of both steady-state heat flux and noise power in moderate coupling regimes, below which we find that these two transport quantities are enhanced by the finite-qubit-energy bias. With external modulations, the geometric-phase-induced heat flux shows a monotonic decrease upon increasing the qubit-bath coupling at zero qubit energy bias (without bias). While under the finite-qubit-energy bias (with bias), the geometric-phase-induced heat flux exhibits an interesting reversal behavior in the strong coupling regime. Our results unify the seemingly contradictory results in weak and strong qubit-bath coupling regimes and provide detailed dissections for the quantum fluctuation of nonequilibrium heat transfer.

  6. Hybrid biasing approaches for global variance reduction

    International Nuclear Information System (INIS)

    Wu, Zeyun; Abdel-Khalik, Hany S.

    2013-01-01

    A new variant of Monte Carlo—deterministic (DT) hybrid variance reduction approach based on Gaussian process theory is presented for accelerating convergence of Monte Carlo simulation and compared with Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) approach implemented in the SCALE package from Oak Ridge National Laboratory. The new approach, denoted the Gaussian process approach, treats the responses of interest as normally distributed random processes. The Gaussian process approach improves the selection of the weight windows of simulated particles by identifying a subspace that captures the dominant sources of statistical response variations. Like the FW-CADIS approach, the Gaussian process approach utilizes particle importance maps obtained from deterministic adjoint models to derive weight window biasing. In contrast to the FW-CADIS approach, the Gaussian process approach identifies the response correlations (via a covariance matrix) and employs them to reduce the computational overhead required for global variance reduction (GVR) purpose. The effective rank of the covariance matrix identifies the minimum number of uncorrelated pseudo responses, which are employed to bias simulated particles. Numerical experiments, serving as a proof of principle, are presented to compare the Gaussian process and FW-CADIS approaches in terms of the global reduction in standard deviation of the estimated responses. - Highlights: ► Hybrid Monte Carlo Deterministic Method based on Gaussian Process Model is introduced. ► Method employs deterministic model to calculate responses correlations. ► Method employs correlations to bias Monte Carlo transport. ► Method compared to FW-CADIS methodology in SCALE code. ► An order of magnitude speed up is achieved for a PWR core model.

  7. The Purine Bias of Coding Sequences is Determined by Physicochemical Constraints on Proteins.

    Science.gov (United States)

    Ponce de Leon, Miguel; de Miranda, Antonio Basilio; Alvarez-Valin, Fernando; Carels, Nicolas

    2014-01-01

    For this report, we analyzed protein secondary structures in relation to the statistics of three nucleotide codon positions. The purpose of this investigation was to find which properties of the ribosome, tRNA or protein level, could explain the purine bias (Rrr) as it is observed in coding DNA. We found that the Rrr pattern is the consequence of a regularity (the codon structure) resulting from physicochemical constraints on proteins and thermodynamic constraints on ribosomal machinery. The physicochemical constraints on proteins mainly come from the hydropathy and molecular weight (MW) of secondary structures as well as the energy cost of amino acid synthesis. These constraints appear through a network of statistical correlations, such as (i) the cost of amino acid synthesis, which is in favor of a higher level of guanine in the first codon position, (ii) the constructive contribution of hydropathy alternation in proteins, (iii) the spatial organization of secondary structure in proteins according to solvent accessibility, (iv) the spatial organization of secondary structure according to amino acid hydropathy, (v) the statistical correlation of MW with protein secondary structures and their overall hydropathy, (vi) the statistical correlation of thymine in the second codon position with hydropathy and the energy cost of amino acid synthesis, and (vii) the statistical correlation of adenine in the second codon position with amino acid complexity and the MW of secondary protein structures. Amino acid physicochemical properties and functional constraints on proteins constitute a code that is translated into a purine bias within the coding DNA via tRNAs. In that sense, the Rrr pattern within coding DNA is the effect of information transfer on nucleotide composition from protein to DNA by selection according to the codon positions. Thus, coding DNA structure and ribosomal machinery co-evolved to minimize the energy cost of protein coding given the functional

  8. Indicators of implicit and explicit social anxiety influence threat-related interpretive bias as a function of working memory capacity

    Directory of Open Access Journals (Sweden)

    Elske eSalemink

    2013-05-01

    Full Text Available Interpretive biases play a crucial role in anxiety disorders. The aim of the current study was to examine factors that determine the relative strength of threat-related interpretive biases that are characteristic of individuals high in social anxiety. Different (dual process models argue that both implicit and explicit processes determine information processing biases and behaviour, and that their impact is moderated by the availability of executive resources such as working memory capacity (WMC. Based on these models, we expected indicators of implicit social anxiety to predict threat-related interpretive bias in individuals low, but not high in WMC. Indicators of explicit social anxiety should predict threat-related interpretive bias in individuals high, but not low in WMC. As expected, WMC moderated the impact of implicit social anxiety on threat-related interpretive bias, although the simple slope for individuals low in WMC was not statistically significant. The hypotheses regarding explicit social anxiety (with fear of negative evaluation used as an indicator were fully supported. The clinical implications of these findings are discussed.

  9. Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making.

    Science.gov (United States)

    Hilbert, Martin

    2012-03-01

    A single coherent framework is proposed to synthesize long-standing research on 8 seemingly unrelated cognitive decision-making biases. During the past 6 decades, hundreds of empirical studies have resulted in a variety of rules of thumb that specify how humans systematically deviate from what is normatively expected from their decisions. Several complementary generative mechanisms have been proposed to explain those cognitive biases. Here it is suggested that (at least) 8 of these empirically detected decision-making biases can be produced by simply assuming noisy deviations in the memory-based information processes that convert objective evidence (observations) into subjective estimates (decisions). An integrative framework is presented to show how similar noise-based mechanisms can lead to conservatism, the Bayesian likelihood bias, illusory correlations, biased self-other placement, subadditivity, exaggerated expectation, the confidence bias, and the hard-easy effect. Analytical tools from information theory are used to explore the nature and limitations that characterize such information processes for binary and multiary decision-making exercises. The ensuing synthesis offers formal mathematical definitions of the biases and their underlying generative mechanism, which permits a consolidated analysis of how they are related. This synthesis contributes to the larger goal of creating a coherent picture that explains the relations among the myriad of seemingly unrelated biases and their potential psychological generative mechanisms. Limitations and research questions are discussed.

  10. Measurement of the $B^-$ lifetime using a simulation free approach for trigger bias correction

    Energy Technology Data Exchange (ETDEWEB)

    Aaltonen, T.; /Helsinki Inst. of Phys.; Adelman, J.; /Chicago U., EFI; Alvarez Gonzalez, B.; /Cantabria Inst. of Phys.; Amerio, S.; /INFN, Padua; Amidei, D.; /Michigan U.; Anastassov, A.; /Northwestern U.; Annovi, A.; /Frascati; Antos, J.; /Comenius U.; Apollinari, G.; /Fermilab; Appel, J.; /Fermilab; Apresyan, A.; /Purdue U. /Waseda U.

    2010-04-01

    The collection of a large number of B hadron decays to hadronic final states at the CDF II detector is possible due to the presence of a trigger that selects events based on track impact parameters. However, the nature of the selection requirements of the trigger introduces a large bias in the observed proper decay time distribution. A lifetime measurement must correct for this bias and the conventional approach has been to use a Monte Carlo simulation. The leading sources of systematic uncertainty in the conventional approach are due to differences between the data and the Monte Carlo simulation. In this paper they present an analytic method for bias correction without using simulation, thereby removing any uncertainty between data and simulation. This method is presented in the form of a measurement of the lifetime of the B{sup -} using the mode B{sup -} {yields} D{sup 0}{pi}{sup -}. The B{sup -} lifetime is measured as {tau}{sub B{sup -}} = 1.663 {+-} 0.023 {+-} 0.015 ps, where the first uncertainty is statistical and the second systematic. This new method results in a smaller systematic uncertainty in comparison to methods that use simulation to correct for the trigger bias.

  11. Measurement of the B- lifetime using a simulation free approach for trigger bias correction

    International Nuclear Information System (INIS)

    2010-01-01

    The collection of a large number of B hadron decays to hadronic final states at the CDF II detector is possible due to the presence of a trigger that selects events based on track impact parameters. However, the nature of the selection requirements of the trigger introduces a large bias in the observed proper decay time distribution. A lifetime measurement must correct for this bias and the conventional approach has been to use a Monte Carlo simulation. The leading sources of systematic uncertainty in the conventional approach are due to differences between the data and the Monte Carlo simulation. In this paper they present an analytic method for bias correction without using simulation, thereby removing any uncertainty between data and simulation. This method is presented in the form of a measurement of the lifetime of the B - using the mode B - → D 0 π - . The B - lifetime is measured as τ B# sup -# = 1.663 ± 0.023 ± 0.015 ps, where the first uncertainty is statistical and the second systematic. This new method results in a smaller systematic uncertainty in comparison to methods that use simulation to correct for the trigger bias.

  12. Social desirability bias in dietary self-report may compromise the validity of dietary intake measures.

    Science.gov (United States)

    Hebert, J R; Clemow, L; Pbert, L; Ockene, I S; Ockene, J K

    1995-04-01

    Self-report of dietary intake could be biased by social desirability or social approval thus affecting risk estimates in epidemiological studies. These constructs produce response set biases, which are evident when testing in domains characterized by easily recognizable correct or desirable responses. Given the social and psychological value ascribed to diet, assessment methodologies used most commonly in epidemiological studies are particularly vulnerable to these biases. Social desirability and social approval biases were tested by comparing nutrient scores derived from multiple 24-hour diet recalls (24HR) on seven randomly assigned days with those from two 7-day diet recalls (7DDR) (similar in some respects to commonly used food frequency questionnaires), one administered at the beginning of the test period (pre) and one at the end (post). Statistical analysis included correlation and multiple linear regression. Cross-sectionally, no relationships between social approval score and the nutritional variables existed. Social desirability score was negatively correlated with most nutritional variables. In linear regression analysis, social desirability score produced a large downward bias in nutrient estimation in the 7DDR relative to the 24HR. For total energy, this bias equalled about 50 kcal/point on the social desirability scale or about 450 kcal over its interquartile range. The bias was approximately twice as large for women as for men and only about half as large in the post measures. Individuals having the highest 24HR-derived fat and total energy intake scores had the largest downward bias due to social desirability. We observed a large downward bias in reporting food intake related to social desirability score. These results are consistent with the theoretical constructs on which the hypothesis is based. The effect of social desirability bias is discussed in terms of its influence on epidemiological estimates of effect. Suggestions are made for future work

  13. Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements

    Energy Technology Data Exchange (ETDEWEB)

    Cunha, Carlos E. [KIPAC, Menlo Park; Huterer, Dragan [Michigan U.; Lin, Huan [Fermilab; Busha, Michael T. [Zurich U.; Wechsler, Risa H. [SLAC

    2014-10-11

    We use N-body-spectro-photometric simulations to investigate the impact of incompleteness and incorrect redshifts in spectroscopic surveys to photometric redshift training and calibration and the resulting effects on cosmological parameter estimation from weak lensing shear-shear correlations. The photometry of the simulations is modeled after the upcoming Dark Energy Survey and the spectroscopy is based on a low/intermediate resolution spectrograph with wavelength coverage of 5500{\\AA} < {\\lambda} < 9500{\\AA}. The principal systematic errors that such a spectroscopic follow-up encounters are incompleteness (inability to obtain spectroscopic redshifts for certain galaxies) and wrong redshifts. Encouragingly, we find that a neural network-based approach can effectively describe the spectroscopic incompleteness in terms of the galaxies' colors, so that the spectroscopic selection can be applied to the photometric sample. Hence, we find that spectroscopic incompleteness yields no appreciable biases to cosmology, although the statistical constraints degrade somewhat because the photometric survey has to be culled to match the spectroscopic selection. Unfortunately, wrong redshifts have a more severe impact: the cosmological biases are intolerable if more than a percent of the spectroscopic redshifts are incorrect. Moreover, we find that incorrect redshifts can also substantially degrade the accuracy of training set based photo-z estimators. The main problem is the difficulty of obtaining redshifts, either spectroscopically or photometrically, for objects at z > 1.3. We discuss several approaches for reducing the cosmological biases, in particular finding that photo-z error estimators can reduce biases appreciably.

  14. Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

    Energy Technology Data Exchange (ETDEWEB)

    Huerta, Gabriel [Univ. of New Mexico, Albuquerque, NM (United States)

    2016-05-10

    The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

  15. Mood-congruent attention and memory bias in dysphoria: Exploring the coherence among information-processing biases.

    Science.gov (United States)

    Koster, Ernst H W; De Raedt, Rudi; Leyman, Lemke; De Lissnyder, Evi

    2010-03-01

    Recent studies indicate that depression is characterized by mood-congruent attention bias at later stages of information-processing. Moreover, depression has been associated with enhanced recall of negative information. The present study tested the coherence between attention and memory bias in dysphoria. Stable dysphoric (n = 41) and non-dysphoric (n = 41) undergraduates first performed a spatial cueing task that included negative, positive, and neutral words. Words were presented for 250 ms under conditions that allowed or prevented elaborate processing. Memory for the words presented in the cueing task was tested using incidental free recall. Dysphoric individuals exhibited an attention bias for negative words in the condition that allowed elaborate processing, with the attention bias for negative words predicting free recall of negative words. Results demonstrate the coherence of attention and memory bias in dysphoric individuals and provide suggestions on the influence of attention bias on further processing of negative material. 2009 Elsevier Ltd. All rights reserved.

  16. p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results.

    Science.gov (United States)

    Simonsohn, Uri; Nelson, Leif D; Simmons, Joseph P

    2014-11-01

    Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the "choice overload" literature. © The Author(s) 2014.

  17. Statistical Analysis of a Comprehensive List of Visual Binaries

    Directory of Open Access Journals (Sweden)

    Kovaleva D.

    2015-12-01

    Full Text Available Visual binary stars are the most abundant class of observed binaries. The most comprehensive list of data on visual binaries compiled recently by cross-matching the largest catalogues of visual binaries allowed a statistical investigation of observational parameters of these systems. The dataset was cleaned by correcting uncertainties and misclassifications, and supplemented with available parallax data. The refined dataset is free from technical biases and contains 3676 presumably physical visual pairs of luminosity class V with known angular separations, magnitudes of the components, spectral types, and parallaxes. We also compiled a restricted sample of 998 pairs free from observational biases due to the probability of binary discovery. Certain distributions of observational and physical parameters of stars of our dataset are discussed.

  18. Lensing corrections to the Eg(z) statistics from large scale structure

    Science.gov (United States)

    Moradinezhad Dizgah, Azadeh; Durrer, Ruth

    2016-09-01

    We study the impact of the often neglected lensing contribution to galaxy number counts on the Eg statistics which is used to constrain deviations from GR. This contribution affects both the galaxy-galaxy and the convergence-galaxy spectra, while it is larger for the latter. At higher redshifts probed by upcoming surveys, for instance at z = 1.5, neglecting this term induces an error of (25-40)% in the spectra and therefore on the Eg statistics which is constructed from the combination of the two. Moreover, including it, renders the Eg statistics scale and bias-dependent and hence puts into question its very objective.

  19. On China's energy intensity statistics: Toward a comprehensive and transparent indicator

    International Nuclear Information System (INIS)

    Wang Xin

    2011-01-01

    A transparent and comprehensive statistical system in China would provide an important basis for enabling a better understanding of the country. This paper focuses on energy intensity (EI), which is one of the most important indicators of China. It firstly reviews China's GDP and energy statistics, showing that China has made great improvements in recent years. The means by which EI data are released and adjusted are then explained. It shows that EI data releases do not provide complete data for calculating EI and constant GDP, which may reduce policy transparency and comprehensiveness. This paper then conducts an EI calculation method that is based on official sources and that respects the data availability of different data release times. It finds that, in general, China's EI statistics can be considered as reliable because most of the results generated by author's calculations match the figures in the official releases. However, two data biases were identified, which may necessitate supplementary information on related constant GDP values used in the official calculation of EI data. The paper concludes by proposing short- and long-term measures for improving EI statistics to provide a transparent and comprehensive EI indicator. - Highlights: → This paper examines data release and adjustment process of energy intensity (EI) target of China. → New insights on the comprehensiveness and transparency of EI data. → Potential data bias between author's calculation and official data due to lack of constant GDP data. → Proposition for improving short- and long-term EI statistical works.

  20. Biases in the OSSOS Detection of Large Semimajor Axis Trans-Neptunian Objects

    Science.gov (United States)

    Gladman, Brett; Shankman, Cory; OSSOS Collaboration

    2017-10-01

    The accumulating but small set of large semimajor axis trans-Neptunian objects (TNOs) shows an apparent clustering in the orientations of their orbits. This clustering must either be representative of the intrinsic distribution of these TNOs, or else have arisen as a result of observation biases and/or statistically expected variations for such a small set of detected objects. The clustered TNOs were detected across different and independent surveys, which has led to claims that the detections are therefore free of observational bias. This apparent clustering has led to the so-called “Planet 9” hypothesis that a super-Earth currently resides in the distant solar system and causes this clustering. The Outer Solar System Origins Survey (OSSOS) is a large program that ran on the Canada-France-Hawaii Telescope from 2013 to 2017, discovering more than 800 new TNOs. One of the primary design goals of OSSOS was the careful determination of observational biases that would manifest within the detected sample. We demonstrate the striking and non-intuitive biases that exist for the detection of TNOs with large semimajor axes. The eight large semimajor axis OSSOS detections are an independent data set, of comparable size to the conglomerate samples used in previous studies. We conclude that the orbital distribution of the OSSOS sample is consistent with being detected from a uniform underlying angular distribution.

  1. Bias in clinical intervention research

    DEFF Research Database (Denmark)

    Gluud, Lise Lotte

    2006-01-01

    Research on bias in clinical trials may help identify some of the reasons why investigators sometimes reach the wrong conclusions about intervention effects. Several quality components for the assessment of bias control have been suggested, but although they seem intrinsically valid, empirical...... evidence is needed to evaluate their effects on the extent and direction of bias. This narrative review summarizes the findings of methodological studies on the influence of bias in clinical trials. A number of methodological studies suggest that lack of adequate randomization in published trial reports...

  2. Stimulus-Driven Attention, Threat Bias, and Sad Bias in Youth with a History of an Anxiety Disorder or Depression.

    Science.gov (United States)

    Sylvester, Chad M; Hudziak, James J; Gaffrey, Michael S; Barch, Deanna M; Luby, Joan L

    2016-02-01

    Attention biases towards threatening and sad stimuli are associated with pediatric anxiety and depression, respectively. The basic cognitive mechanisms associated with attention biases in youth, however, remain unclear. Here, we tested the hypothesis that threat bias (selective attention for threatening versus neutral stimuli) but not sad bias relies on stimulus-driven attention. We collected measures of stimulus-driven attention, threat bias, sad bias, and current clinical symptoms in youth with a history of an anxiety disorder and/or depression (ANX/DEP; n = 40) as well as healthy controls (HC; n = 33). Stimulus-driven attention was measured with a non-emotional spatial orienting task, while threat bias and sad bias were measured at a short time interval (150 ms) with a spatial orienting task using emotional faces and at a longer time interval (500 ms) using a dot-probe task. In ANX/DEP but not HC, early attention bias towards threat was negatively correlated with later attention bias to threat, suggesting that early threat vigilance was associated with later threat avoidance. Across all subjects, stimulus-driven orienting was not correlated with early threat bias but was negatively correlated with later threat bias, indicating that rapid stimulus-driven orienting is linked to later threat avoidance. No parallel relationships were detected for sad bias. Current symptoms of depression but not anxiety were related to decreased stimulus-driven attention. Together, these results are consistent with the hypothesis that threat bias but not sad bias relies on stimulus-driven attention. These results inform the design of attention bias modification programs that aim to reverse threat biases and reduce symptoms associated with pediatric anxiety and depression.

  3. Stimulus-driven attention, threat bias, and sad bias in youth with a history of an anxiety disorder or depression

    Science.gov (United States)

    Sylvester, Chad M.; Hudziak, James J.; Gaffrey, Michael S.; Barch, Deanna M.; Luby, Joan L.

    2015-01-01

    Attention biases towards threatening and sad stimuli are associated with pediatric anxiety and depression, respectively. The basic cognitive mechanisms associated with attention biases in youth, however, remain unclear. Here, we tested the hypothesis that threat bias (selective attention for threatening versus neutral stimuli) but not sad bias relies on stimulus-driven attention. We collected measures of stimulus-driven attention, threat bias, sad bias, and current clinical symptoms in youth with a history of an anxiety disorder and/or depression (ANX/DEP; n=40) as well as healthy controls (HC; n=33). Stimulus-driven attention was measured with a non-emotional spatial orienting task, while threat bias and sad bias were measured at a short time interval (150 ms) with a spatial orienting task using emotional faces and at a longer time interval (500 ms) using a dot-probe task. In ANX/DEP but not HC, early attention bias towards threat was negatively correlated with later attention bias to threat, suggesting that early threat vigilance was associated with later threat avoidance. Across all subjects, stimulus-driven orienting was not correlated with early threat bias but was negatively correlated with later threat bias, indicating that rapid stimulus-driven orienting is linked to later threat avoidance. No parallel relationships were detected for sad bias. Current symptoms of depression but not anxiety were related to decreased stimulus-driven attention. Together, these results are consistent with the hypothesis that threat bias but not sad bias relies on stimulus-driven attention. These results inform the design of attention bias modification programs that aim to reverse threat biases and reduce symptoms associated with pediatric anxiety and depression. PMID:25702927

  4. Survivor bias in Mendelian randomization analysis

    DEFF Research Database (Denmark)

    Vansteelandt, Stijn; Dukes, Oliver; Martinussen, Torben

    2017-01-01

    Mendelian randomization studies employ genotypes as experimental handles to infer the effect of genetically modified exposures (e.g. vitamin D exposure) on disease outcomes (e.g. mortality). The statistical analysis of these studies makes use of the standard instrumental variables framework. Many...... of these studies focus on elderly populations, thereby ignoring the problem of left truncation, which arises due to the selection of study participants being conditional upon surviving up to the time of study onset. Such selection, in general, invalidates the assumptions on which the instrumental variables...... analysis rests. We show that Mendelian randomization studies of adult or elderly populations will therefore, in general, return biased estimates of the exposure effect when the considered genotype affects mortality; in contrast, standard tests of the causal null hypothesis that the exposure does not affect...

  5. Benefits of being biased!

    Indian Academy of Sciences (India)

    Administrator

    Journal of Genetics, Vol. 83, No. 2, August 2004. Keywords. codon bias; alcohol dehydrogenase; Darwinian ... RESEARCH COMMENTARY. Benefits of being biased! SUTIRTH DEY*. Evolutionary Biology Laboratory, Evolutionary & Organismal Biology Unit,. Jawaharlal Nehru Centre for Advanced Scientific Research,.

  6. Gender bias in clinical case reports: A cross-sectional study of the "big five" medical journals.

    Directory of Open Access Journals (Sweden)

    Pascale Allotey

    Full Text Available Gender bias in medical journals can affect the science and the benefit to patients. It has never been investigated in clinical case reports. The oversight is important because of the role clinical case reports play in hypothesis generation and medical education. We investigated contemporary gender bias in case reports for the highest ranked journals in general and internal medicine.PubMed case reports data from 2011 to 2016 were extracted for the Annals of Internal Medicine, British Medical Journal, the Journal of the American Medical Association, The Lancet, and New England Journal of Medicine. The gender of the patients were identified and a text analysis of the Medical Subject Headings conducted.A total of 2,742 case reports were downloaded and 2,582 (95.6% reports contributed to the final analysis. A pooled analysis showed a statistically significant gender bias against female case reports (0.45; 95%CI: 0.43-0.47. The Annals of Internal Medicine was the only journal with a point estimate (non significant in the direction of a bias against male patients. The text analysis identified no substantive difference in the focus of the case reports and no obvious explanation for the bias.Gender bias, previously identified in clinical research and in clinical authorship, extends into the patients presented in clinical case reports. Whether it is driven by authors or editors is not clear, but it likely contributes to and supports an overall male bias of clinical medicine.

  7. Biases in GNSS-Data Processing

    Science.gov (United States)

    Schaer, S. C.; Dach, R.; Lutz, S.; Meindl, M.; Beutler, G.

    2010-12-01

    Within the Global Positioning System (GPS) traditionally different types of pseudo-range measurements (P-code, C/A-code) are available on the first frequency that are tracked by the receivers with different technologies. For that reason, P1-C1 and P1-P2 Differential Code Biases (DCB) need to be considered in a GPS data processing with a mix of different receiver types. Since the Block IIR-M series of GPS satellites also provide C/A-code on the second frequency, P2-C2 DCB need to be added to the list of biases for maintenance. Potential quarter-cycle biases between different phase observables (specifically L2P and L2C) are another issue. When combining GNSS (currently GPS and GLONASS), careful consideration of inter-system biases (ISB) is indispensable, in particular when an adequate combination of individual GLONASS clock correction results from different sources (using, e.g., different software packages) is intended. Facing the GPS and GLONASS modernization programs and the upcoming GNSS, like the European Galileo and the Chinese Compass, an increasing number of types of biases is expected. The Center for Orbit Determination in Europe (CODE) is monitoring these GPS and GLONASS related biases for a long time based on RINEX files of the tracking network of the International GNSS Service (IGS) and in the frame of the data processing as one of the global analysis centers of the IGS. Within the presentation we give an overview on the stability of the biases based on the monitoring. Biases derived from different sources are compared. Finally, we give an outlook on the potential handling of such biases with the big variety of signals and systems expected in the future.

  8. Bias aware Kalman filters

    DEFF Research Database (Denmark)

    Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan

    2006-01-01

    This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state....... The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved....

  9. Investment, managerial capacity, and bias in public health preparedness.

    Science.gov (United States)

    Langabeer, James R; DelliFraine, Jami L; Tyson, Sandra; Emert, Jamie M; Herbold, John

    2009-01-01

    Nearly $7 billion has been invested through national cooperative funding since 2002 to strengthen state and local response capacity. Yet, very little outcome evidence exists to analyze funding effectiveness. The objective of this research is to analyze the relationship between investment (funding) and capacity (readiness) for public health preparedness (PHP). The aim of the authors is to use a management framework to evaluate capacity, and to explore the "immediacy bias" impact on investment stability. This study employs a longitudinal study design, incorporating survey research of the entire population of 68 health departments in the state of Texas. The authors assessed the investment-capacity relationship through several statistical methods. The authors created a structural measure of managerial capacity through principal components analysis, factorizing 10 independent variables and augment this with a perceived readiness level reported from PHP managers. The authors then employ analysis of variance, correlation analyses, and other descriptive statistics. There has been a 539 percent coefficient of variation in funding at the local level between the years 2004 and 2008, and a 63 percent reduction in total resources since the peak of funding, using paired sample data. Results suggest that investment is positively associated with readiness and managerial capacity in local health departments. The authors also find that investment was related to greater community collaboration, higher adoption of Incident Command System (ICS) structure, and more frequent operational drills and exercises. Greater investment is associated with higher levels of capacity and readiness. The authors conclude from this that investment should be stabilized and continued, and not be influenced by historical cognitive biases.

  10. Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.

    Science.gov (United States)

    Leyrat, Clémence; Caille, Agnès; Foucher, Yohann; Giraudeau, Bruno

    2016-01-22

    Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.

  11. Measuring Agricultural Bias

    DEFF Research Database (Denmark)

    Jensen, Henning Tarp; Robinson, Sherman; Tarp, Finn

    The measurement issue is the key issue in the literature on trade policy-induced agri-cultural price incentive bias. This paper introduces a general equilibrium effective rate of protection (GE-ERP) measure, which extends and generalizes earlier partial equilibrium nominal protection measures...... shares and intersectoral linkages - are crucial for determining the sign and magnitude of trade policy bias. The GE-ERP measure is therefore uniquely suited to capture the full impact of trade policies on agricultural price incentives. A Monte Carlo procedure confirms that the results are robust....... For the 15 sample countries, the results indicate that the agricultural price incentive bias, which was generally perceived to exist during the 1980s, was largely eliminated during the 1990s. The results also demonstrate that general equilibrium effects and country-specific characteristics - including trade...

  12. Statistical learning is constrained to less abstract patterns in complex sensory input (but not the least).

    Science.gov (United States)

    Emberson, Lauren L; Rubinstein, Dani Y

    2016-08-01

    The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1-dog1, bird2-dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1-dog_picture1, bird_picture2-dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual

  13. Statistical methods of parameter estimation for deterministically chaotic time series

    Science.gov (United States)

    Pisarenko, V. F.; Sornette, D.

    2004-03-01

    We discuss the possibility of applying some standard statistical methods (the least-square method, the maximum likelihood method, and the method of statistical moments for estimation of parameters) to deterministically chaotic low-dimensional dynamic system (the logistic map) containing an observational noise. A “segmentation fitting” maximum likelihood (ML) method is suggested to estimate the structural parameter of the logistic map along with the initial value x1 considered as an additional unknown parameter. The segmentation fitting method, called “piece-wise” ML, is similar in spirit but simpler and has smaller bias than the “multiple shooting” previously proposed. Comparisons with different previously proposed techniques on simulated numerical examples give favorable results (at least, for the investigated combinations of sample size N and noise level). Besides, unlike some suggested techniques, our method does not require the a priori knowledge of the noise variance. We also clarify the nature of the inherent difficulties in the statistical analysis of deterministically chaotic time series and the status of previously proposed Bayesian approaches. We note the trade off between the need of using a large number of data points in the ML analysis to decrease the bias (to guarantee consistency of the estimation) and the unstable nature of dynamical trajectories with exponentially fast loss of memory of the initial condition. The method of statistical moments for the estimation of the parameter of the logistic map is discussed. This method seems to be the unique method whose consistency for deterministically chaotic time series is proved so far theoretically (not only numerically).

  14. Administrative bias in South Africa

    Directory of Open Access Journals (Sweden)

    E S Nwauche

    2005-01-01

    Full Text Available This article reviews the interpretation of section 6(2(aii of the Promotion of Administrative Justice Act which makes an administrator “biased or reasonably suspected of bias” a ground of judicial review. In this regard, the paper reviews the determination of administrative bias in South Africa especially highlighting the concept of institutional bias. The paper notes that inspite of the formulation of the bias ground of review the test for administrative bias is the reasonable apprehension test laid down in the case of President of South Africa v South African Rugby Football Union(2 which on close examination is not the same thing. Accordingly the paper urges an alternative interpretation that is based on the reasonable suspicion test enunciated in BTR Industries South Africa (Pty Ltd v Metal and Allied Workers Union and R v Roberts. Within this context, the paper constructs a model for interpreting the bias ground of review that combines the reasonable suspicion test as interpreted in BTR Industries and R v Roberts, the possibility of the waiver of administrative bias, the curative mechanism of administrative appeal as well as some level of judicial review exemplified by the jurisprudence of article 6(1 of the European Convention of Human Rights, especially in the light of the contemplation of the South African Magistrate Court as a jurisdictional route of judicial review.

  15. Attention, interpretation, and memory biases in subclinical depression: a proof-of-principle test of the combined cognitive biases hypothesis.

    Science.gov (United States)

    Everaert, Jonas; Duyck, Wouter; Koster, Ernst H W

    2014-04-01

    Emotional biases in attention, interpretation, and memory are viewed as important cognitive processes underlying symptoms of depression. To date, there is a limited understanding of the interplay among these processing biases. This study tested the dependence of memory on depression-related biases in attention and interpretation. Subclinically depressed and nondepressed participants completed a computerized version of the scrambled sentences test (measuring interpretation bias) while their eye movements were recorded (measuring attention bias). This task was followed by an incidental free recall test of previously constructed interpretations (measuring memory bias). Path analysis revealed a good fit for the model in which selective orienting of attention was associated with interpretation bias, which in turn was associated with a congruent bias in memory. Also, a good fit was observed for a path model in which biases in the maintenance of attention and interpretation were associated with memory bias. Both path models attained a superior fit compared with path models without the theorized functional relations among processing biases. These findings enhance understanding of how mechanisms of attention and interpretation regulate what is remembered. As such, they offer support for the combined cognitive biases hypothesis or the notion that emotionally biased cognitive processes are not isolated mechanisms but instead influence each other. Implications for theoretical models and emotion regulation across the spectrum of depressive symptoms are discussed.

  16. Bias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!

    Science.gov (United States)

    Vetter, Thomas R; Mascha, Edward J

    2017-09-01

    the association or treatment effect. Interaction among variables, also known as effect modification, exists when the effect of 1 explanatory variable on the outcome depends on the particular level or value of another explanatory variable. Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.

  17. Preferences, country bias, and international trade

    NARCIS (Netherlands)

    S. Roy (Santanu); J.M.A. Viaene (Jean-Marie)

    1998-01-01

    textabstractAnalyzes international trade where consumer preferences exhibit country bias. Why country biases arise; How trade can occur in the presence of country bias; Implication for the pattern of trade and specialization.

  18. Biases in casino betting

    Directory of Open Access Journals (Sweden)

    James Sundali

    2006-07-01

    Full Text Available We examine two departures of individual perceptions of randomness from probability theory: the hot hand and the gambler's fallacy, and their respective opposites. This paper's first contribution is to use data from the field (individuals playing roulette in a casino to demonstrate the existence and impact of these biases that have been previously documented in the lab. Decisions in the field are consistent with biased beliefs, although we observe significant individual heterogeneity in the population. A second contribution is to separately identify these biases within a given individual, then to examine their within-person correlation. We find a positive and significant correlation across individuals between hot hand and gambler's fallacy biases, suggesting a common (root cause of the two related errors. We speculate as to the source of this correlation (locus of control, and suggest future research which could test this speculation.

  19. Transcriptional profiling of human liver identifies sex-biased genes associated with polygenic dyslipidemia and coronary artery disease.

    Directory of Open Access Journals (Sweden)

    Yijing Zhang

    Full Text Available Sex-differences in human liver gene expression were characterized on a genome-wide scale using a large liver sample collection, allowing for detection of small expression differences with high statistical power. 1,249 sex-biased genes were identified, 70% showing higher expression in females. Chromosomal bias was apparent, with female-biased genes enriched on chrX and male-biased genes enriched on chrY and chr19, where 11 male-biased zinc-finger KRAB-repressor domain genes are distributed in six clusters. Top biological functions and diseases significantly enriched in sex-biased genes include transcription, chromatin organization and modification, sexual reproduction, lipid metabolism and cardiovascular disease. Notably, sex-biased genes are enriched at loci associated with polygenic dyslipidemia and coronary artery disease in genome-wide association studies. Moreover, of the 8 sex-biased genes at these loci, 4 have been directly linked to monogenic disorders of lipid metabolism and show an expression profile in females (elevated expression of ABCA1, APOA5 and LDLR; reduced expression of LIPC that is consistent with the lower female risk of coronary artery disease. Female-biased expression was also observed for CYP7A1, which is activated by drugs used to treat hypercholesterolemia. Several sex-biased drug-metabolizing enzyme genes were identified, including members of the CYP, UGT, GPX and ALDH families. Half of 879 mouse orthologs, including many genes of lipid metabolism and homeostasis, show growth hormone-regulated sex-biased expression in mouse liver, suggesting growth hormone might play a similar regulatory role in human liver. Finally, the evolutionary rate of protein coding regions for human-mouse orthologs, revealed by dN/dS ratio, is significantly higher for genes showing the same sex-bias in both species than for non-sex-biased genes. These findings establish that human hepatic sex differences are widespread and affect diverse cell

  20. An inclusive taxonomy of behavioral biases

    Directory of Open Access Journals (Sweden)

    David Peón

    2017-07-01

    Full Text Available This paper overviews the theoretical and empirical research on behavioral biases and their influence in the literature. To provide a systematic exposition, we present a unified framework that takes the reader through an original taxonomy, based on the reviews of relevant authors in the field. In particular, we establish three broad categories that may be distinguished: heuristics and biases; choices, values and frames; and social factors. We then describe the main biases within each category, and revise the main theoretical and empirical developments, linking each bias with other biases and anomalies that are related to them, according to the literature.

  1. INDIVIDUAL INVESTMENT DECISION MAKING PROCESS. BIASES AND REMEDIES

    Directory of Open Access Journals (Sweden)

    ADRIAN MITROI

    2014-02-01

    Full Text Available ary In the pursuit of understanding the behavior of the market player, the basic argument relays on the supposition that the risk appetite increases exactly at the worst moment - when the capacity to assume additional risk decreases significantly. People view a sample randomly drawn from a population as highly representative and cvasi similar to the population in all its essential characteristics. They expect any two samples drawn from a particular population to be more similar to one another and to the population than is statistically justifiable. This behavior is different from the tenets of classic finance theory. The article reviews some psychological concepts relevant and used in the study, in an interdisciplinary effort of understanding the correlation or causality between psychology and finance. The statistical interrogation describes the sampling methodology, the frequency of data and the empirical methodology that lead to analysis of the results and concluding remarks. The study provides details on raw statistical test scores, regression results and analysis. In this study, I evaluate the association between investors’ behavior and her portfolio results. The paper aims at demonstrating whether investor psychological biases lead to investment performance to tilt to the mean in the long run.

  2. Statistical methods for quantitative mass spectrometry proteomic experiments with labeling

    Directory of Open Access Journals (Sweden)

    Oberg Ann L

    2012-11-01

    Full Text Available Abstract Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.

  3. Statistical methods for quantitative mass spectrometry proteomic experiments with labeling.

    Science.gov (United States)

    Oberg, Ann L; Mahoney, Douglas W

    2012-01-01

    Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.

  4. Probing Biased Signaling in Chemokine Receptors

    DEFF Research Database (Denmark)

    Amarandi, Roxana Maria; Hjortø, Gertrud Malene; Rosenkilde, Mette Marie

    2016-01-01

    The chemokine system mediates leukocyte migration during homeostatic and inflammatory processes. Traditionally, it is described as redundant and promiscuous, with a single chemokine ligand binding to different receptors and a single receptor having several ligands. Signaling of chemokine receptors...... of others has been termed signaling bias and can accordingly be grouped into ligand bias, receptor bias, and tissue bias. Bias has so far been broadly overlooked in the process of drug development. The low number of currently approved drugs targeting the chemokine system, as well as the broad range...... of failed clinical trials, reflects the need for a better understanding of the chemokine system. Thus, understanding the character, direction, and consequence of biased signaling in the chemokine system may aid the development of new therapeutics. This review describes experiments to assess G protein...

  5. Bias-field equalizer for bubble memories

    Science.gov (United States)

    Keefe, G. E.

    1977-01-01

    Magnetoresistive Perm-alloy sensor monitors bias field required to maintain bubble memory. Sensor provides error signal that, in turn, corrects magnitude of bias field. Error signal from sensor can be used to control magnitude of bias field in either auxiliary set of bias-field coils around permanent magnet field, or current in small coils used to remagnetize permanent magnet by infrequent, short, high-current pulse or short sequence of pulses.

  6. A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

    Science.gov (United States)

    Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

    2017-07-01

    Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

  7. GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data.

    Directory of Open Access Journals (Sweden)

    Borbala Mifsud

    Full Text Available Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html.

  8. GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data.

    Science.gov (United States)

    Mifsud, Borbala; Martincorena, Inigo; Darbo, Elodie; Sugar, Robert; Schoenfelder, Stefan; Fraser, Peter; Luscombe, Nicholas M

    2017-01-01

    Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html).

  9. Prospective elementary and secondary school mathematics teachers’ statistical reasoning

    Directory of Open Access Journals (Sweden)

    Rabia KARATOPRAK

    2015-04-01

    Full Text Available This study investigated prospective elementary (PEMTs and secondary (PSMTs school mathematics teachers’ statistical reasoning. The study began with the adaptation of the Statistical Reasoning Assessment (Garfield, 2003 test. Then, the test was administered to 82 PEMTs and 91 PSMTs in a metropolitan city of Turkey. Results showed that both groups were equally successful in understanding independence, and understanding importance of large samples. However, results from selecting appropriate measures of center together with the misconceptions assessing the same subscales showed that both groups selected mode rather than mean as an appropriate average. This suggested their lack of attention to the categorical and interval/ratio variables while examining data. Similarly, both groups were successful in interpreting and computing probability; however, they had equiprobability bias, law of small numbers and representativeness misconceptions. The results imply a change in some questions in the Statistical Reasoning Assessment test and that teacher training programs should include statistics courses focusing on studying characteristics of samples.

  10. Introduction to Unconscious Bias

    Science.gov (United States)

    Schmelz, Joan T.

    2010-05-01

    We all have biases, and we are (for the most part) unaware of them. In general, men and women BOTH unconsciously devalue the contributions of women. This can have a detrimental effect on grant proposals, job applications, and performance reviews. Sociology is way ahead of astronomy in these studies. When evaluating identical application packages, male and female University psychology professors preferred 2:1 to hire "Brian” over "Karen” as an assistant professor. When evaluating a more experienced record (at the point of promotion to tenure), reservations were expressed four times more often when the name was female. This unconscious bias has a repeated negative effect on Karen's career. This talk will introduce the concept of unconscious bias and also give recommendations on how to address it using an example for a faculty search committee. The process of eliminating unconscious bias begins with awareness, then moves to policy and practice, and ends with accountability.

  11. First-impression bias effects on mismatch negativity to auditory spatial deviants.

    Science.gov (United States)

    Fitzgerald, Kaitlin; Provost, Alexander; Todd, Juanita

    2018-04-01

    Internal models of regularities in the world serve to facilitate perception as redundant input can be predicted and neural resources conserved for that which is new or unexpected. In the auditory system, this is reflected in an evoked potential component known as mismatch negativity (MMN). MMN is elicited by the violation of an established regularity to signal the inaccuracy of the current model and direct resources to the unexpected event. Prevailing accounts suggest that MMN amplitude will increase with stability in regularity; however, observations of first-impression bias contradict stability effects. If tones rotate probabilities as a rare deviant (p = .125) and common standard (p = .875), MMN elicited to the initial deviant tone reaches maximal amplitude faster than MMN to the first standard when later encountered as deviant-a differential pattern that persists throughout rotations. Sensory inference is therefore biased by longer-term contextual information beyond local probability statistics. Using the same multicontext sequence structure, we examined whether this bias generalizes to MMN elicited by spatial sound cues using monaural sounds (n = 19, right first deviant and n = 22, left first deviant) and binaural sounds (n = 19, right first deviant). The characteristic differential modulation of MMN to the two tones was observed in two of three groups, providing partial support for the generalization of first-impression bias to spatially deviant sounds. We discuss possible explanations for its absence when the initial deviant was delivered monaurally to the right ear. © 2017 Society for Psychophysiological Research.

  12. What we don't know can hurt us: Nonresponse bias assessment in birth defects research.

    Science.gov (United States)

    Strassle, Paula D; Cassell, Cynthia H; Shapira, Stuart K; Tinker, Sarah C; Meyer, Robert E; Grosse, Scott D

    2015-07-01

    Nonresponse bias assessment is an important and underutilized tool in survey research to assess potential bias due to incomplete participation. This study illustrates a nonresponse bias sensitivity assessment using a survey on perceived barriers to care for children with orofacial clefts in North Carolina. Children born in North Carolina between 2001 and 2004 with an orofacial cleft were eligible for inclusion. Vital statistics data, including maternal and child characteristics, were available on all eligible subjects. Missing 'responses' from nonparticipants were imputed using assumptions based on the distribution of responses, survey method (mail or phone), and participant maternal demographics. Overall, 245 of 475 subjects (51.6%) responded to either a mail or phone survey. Cost as a barrier to care was reported by 25.0% of participants. When stratified by survey type, 28.3% of mail respondents and 17.2% of phone respondents reported cost as a barrier. Under various assumptions, the bias-adjusted estimated prevalence of cost as barrier to care ranged from 16.1% to 30.0%. Maternal age, education, race, and marital status at time of birth were not associated with subjects reporting cost as a barrier. As survey response rates continue to decline, the importance of assessing the potential impact of nonresponse bias has become more critical. Birth defects research is particularly conducive to nonresponse bias analysis, especially when birth defect registries and birth certificate records are used. Future birth defect studies which use population-based surveillance data and have incomplete participation could benefit from this type of nonresponse bias assessment. Birth Defects Research (Part A) 103:603-609, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  13. Research bias in judgement bias studies : a systematic review of valuation judgement literature

    NARCIS (Netherlands)

    Vincent Gruis; Pim Klamer; Cok Bakker

    2017-01-01

    Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research

  14. Research bias in judgement bias studies : A systematic review of valuation judgement literature

    NARCIS (Netherlands)

    Klamer, Pim; Bakker, C.; Gruis, Vincent

    2017-01-01

    Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research

  15. Information filtering via biased heat conduction

    Science.gov (United States)

    Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

    2011-09-01

    The process of heat conduction has recently found application in personalized recommendation [Zhou , Proc. Natl. Acad. Sci. USA PNASA60027-842410.1073/pnas.1000488107107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

  16. The lack of selection bias in a snowball sampled case-control study on drug abuse.

    Science.gov (United States)

    Lopes, C S; Rodrigues, L C; Sichieri, R

    1996-12-01

    Friend controls in matched case-control studies can be a potential source of bias based on the assumption that friends are more likely to share exposure factors. This study evaluates the role of selection bias in a case-control study that used the snowball sampling method based on friendship for the selection of cases and controls. The cases selected fro the study were drug abusers located in the community. Exposure was defined by the presence of at least one psychiatric diagnosis. Psychiatric and drug abuse/dependence diagnoses were made according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) criteria. Cases and controls were matched on sex, age and friendship. The measurement of selection bias was made through the comparison of the proportion of exposed controls selected by exposed cases (p1) with the proportion of exposed controls selected by unexposed cases (p2). If p1 = p2 then, selection bias should not occur. The observed distribution of the 185 matched pairs having at least one psychiatric disorder showed a p1 value of 0.52 and a p2 value of 0.51, indicating no selection bias in this study. Our findings support the idea that the use of friend controls can produce a valid basis for a case-control study.

  17. Obesity bias among health and non-health students attending an Australian university and their perceived obesity education.

    Science.gov (United States)

    Robinson, Emma L; Ball, Lauren E; Leveritt, Michael D

    2014-01-01

    This study compared the level of prejudice against obese individuals (obesity bias) among final-year health and non-health students, and associated obesity education. Cross-sectional online survey of 479 final-year students (292 health and 187 non-health) from Griffith University, Australia. Implicit and explicit obesity bias was measured using validated tools, and perceived obesity education ranked from "none" to "excellent." Data were analyzed quantitatively using analysis of variance and independent sample t tests. Statistical significance was set at P Students' mean age was 26.2 ± 7.6 years and body mass index was 23.2 ± 4.7 kg/m(2). Health and non-health students exhibited significant levels of obesity bias. Non-health students were more likely to suggest that obese individuals lacked willpower (P = .03). Students' self-reported obesity education varied considerably. Those who reported a higher level of genetics-related obesity education were less likely to believe that obese individuals were "bad" (P = .002) or to show concern about putting on weight (P = .01). Obesity bias exists in health students in Australia and is similar to non-health students' obesity bias levels. Students' self-reported genetics-related obesity education may be associated with obesity bias. Modifications to existing health curricula should be considered to reduce obesity bias among future health professionals. Copyright © 2014 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

  18. Insights on the evolution of metabolic networks of unicellular translationally biased organisms from transcriptomic data and sequence analysis.

    Science.gov (United States)

    Carbone, Alessandra; Madden, Richard

    2005-10-01

    Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.

  19. Attention bias modification training under working memory load increases the magnitude of change in attentional bias.

    Science.gov (United States)

    Clarke, Patrick J F; Branson, Sonya; Chen, Nigel T M; Van Bockstaele, Bram; Salemink, Elske; MacLeod, Colin; Notebaert, Lies

    2017-12-01

    Attention bias modification (ABM) procedures have shown promise as a therapeutic intervention, however current ABM procedures have proven inconsistent in their ability to reliably achieve the requisite change in attentional bias needed to produce emotional benefits. This highlights the need to better understand the precise task conditions that facilitate the intended change in attention bias in order to realise the therapeutic potential of ABM procedures. Based on the observation that change in attentional bias occurs largely outside conscious awareness, the aim of the current study was to determine if an ABM procedure delivered under conditions likely to preclude explicit awareness of the experimental contingency, via the addition of a working memory load, would contribute to greater change in attentional bias. Bias change was assessed among 122 participants in response to one of four ABM tasks given by the two experimental factors of ABM training procedure delivered either with or without working memory load, and training direction of either attend-negative or avoid-negative. Findings revealed that avoid-negative ABM procedure under working memory load resulted in significantly greater reductions in attentional bias compared to the equivalent no-load condition. The current findings will require replication with clinical samples to determine the utility of the current task for achieving emotional benefits. These present findings are consistent with the position that the addition of a working memory load may facilitate change in attentional bias in response to an ABM training procedure. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

    Science.gov (United States)

    Lange, Stefan

    2018-05-01

    Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

  1. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

    Directory of Open Access Journals (Sweden)

    S. Lange

    2018-05-01

    Full Text Available Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds. Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016 rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011 data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016. This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

  2. Attribution bias and social anxiety in schizophrenia

    Directory of Open Access Journals (Sweden)

    Amelie M. Achim

    2016-06-01

    Full Text Available Studies on attribution biases in schizophrenia have produced mixed results, whereas such biases have been more consistently reported in people with anxiety disorders. Anxiety comorbidities are frequent in schizophrenia, in particular social anxiety disorder, which could influence their patterns of attribution biases. The objective of the present study was thus to determine if individuals with schizophrenia and a comorbid social anxiety disorder (SZ+ show distinct attribution biases as compared with individuals with schizophrenia without social anxiety (SZ− and healthy controls. Attribution biases were assessed with the Internal, Personal, and Situational Attributions Questionnaire in 41 individual with schizophrenia and 41 healthy controls. Results revealed the lack of the normal externalizing bias in SZ+, whereas SZ− did not significantly differ from healthy controls on this dimension. The personalizing bias was not influenced by social anxiety but was in contrast linked with delusions, with a greater personalizing bias in individuals with current delusions. Future studies on attribution biases in schizophrenia should carefully document symptom presentation, including social anxiety.

  3. Can statistic adjustment of OR minimize the potential confounding bias for meta-analysis of case-control study? A secondary data analysis.

    Science.gov (United States)

    Liu, Tianyi; Nie, Xiaolu; Wu, Zehao; Zhang, Ying; Feng, Guoshuang; Cai, Siyu; Lv, Yaqi; Peng, Xiaoxia

    2017-12-29

    Different confounder adjustment strategies were used to estimate odds ratios (ORs) in case-control study, i.e. how many confounders original studies adjusted and what the variables are. This secondary data analysis is aimed to detect whether there are potential biases caused by difference of confounding factor adjustment strategies in case-control study, and whether such bias would impact the summary effect size of meta-analysis. We included all meta-analyses that focused on the association between breast cancer and passive smoking among non-smoking women, as well as each original case-control studies included in these meta-analyses. The relative deviations (RDs) of each original study were calculated to detect how magnitude the adjustment would impact the estimation of ORs, compared with crude ORs. At the same time, a scatter diagram was sketched to describe the distribution of adjusted ORs with different number of adjusted confounders. Substantial inconsistency existed in meta-analysis of case-control studies, which would influence the precision of the summary effect size. First, mixed unadjusted and adjusted ORs were used to combine individual OR in majority of meta-analysis. Second, original studies with different adjustment strategies of confounders were combined, i.e. the number of adjusted confounders and different factors being adjusted in each original study. Third, adjustment did not make the effect size of original studies trend to constringency, which suggested that model fitting might have failed to correct the systematic error caused by confounding. The heterogeneity of confounder adjustment strategies in case-control studies may lead to further bias for summary effect size in meta-analyses, especially for weak or medium associations so that the direction of causal inference would be even reversed. Therefore, further methodological researches are needed, referring to the assessment of confounder adjustment strategies, as well as how to take this kind

  4. Biased Brownian dynamics for rate constant calculation.

    OpenAIRE

    Zou, G; Skeel, R D; Subramaniam, S

    2000-01-01

    An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampl...

  5. The coalitional value theory of antigay bias

    NARCIS (Netherlands)

    Winegard, Bo; Reynolds, Tania; Baumeister, Roy F.; Plant, E. Ashby

    2016-01-01

    Research indicates that antigay bias follows a specific pattern (and probably has throughout written history, at least in the West): (a) men evince more antigay bias than women; (b) men who belong to traditionally male coalitions evince more antigay bias than those who do not; (c) antigay bias is

  6. Symmetry as Bias: Rediscovering Special Relativity

    Science.gov (United States)

    Lowry, Michael R.

    1992-01-01

    This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

  7. Progressive statistics for studies in sports medicine and exercise science.

    Science.gov (United States)

    Hopkins, William G; Marshall, Stephen W; Batterham, Alan M; Hanin, Juri

    2009-01-01

    Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.

  8. Anti-Bias Education: Reflections

    Science.gov (United States)

    Derman-Sparks, Louise

    2011-01-01

    It is 30 years since NAEYC published "Anti-Bias Curriculum Tools for Empowering Young Children" (Derman-Sparks & ABC Task Force, 1989). Since then, anti-bias education concepts have become part of the early childhood education (ECE) narrative in the United States and many other countries. It has brought a fresh way of thinking about…

  9. Anatomy of the Higgs fits: A first guide to statistical treatments of the theoretical uncertainties

    Directory of Open Access Journals (Sweden)

    Sylvain Fichet

    2016-04-01

    Full Text Available The studies of the Higgs boson couplings based on the recent and upcoming LHC data open up a new window on physics beyond the Standard Model. In this paper, we propose a statistical guide to the consistent treatment of the theoretical uncertainties entering the Higgs rate fits. Both the Bayesian and frequentist approaches are systematically analysed in a unified formalism. We present analytical expressions for the marginal likelihoods, useful to implement simultaneously the experimental and theoretical uncertainties. We review the various origins of the theoretical errors (QCD, EFT, PDF, production mode contamination…. All these individual uncertainties are thoroughly combined with the help of moment-based considerations. The theoretical correlations among Higgs detection channels appear to affect the location and size of the best-fit regions in the space of Higgs couplings. We discuss the recurrent question of the shape of the prior distributions for the individual theoretical errors and find that a nearly Gaussian prior arises from the error combinations. We also develop the bias approach, which is an alternative to marginalisation providing more conservative results. The statistical framework to apply the bias principle is introduced and two realisations of the bias are proposed. Finally, depending on the statistical treatment, the Standard Model prediction for the Higgs signal strengths is found to lie within either the 68% or 95% confidence level region obtained from the latest analyses of the 7 and 8 TeV LHC datasets.

  10. Anatomy of the Higgs fits: A first guide to statistical treatments of the theoretical uncertainties

    Science.gov (United States)

    Fichet, Sylvain; Moreau, Grégory

    2016-04-01

    The studies of the Higgs boson couplings based on the recent and upcoming LHC data open up a new window on physics beyond the Standard Model. In this paper, we propose a statistical guide to the consistent treatment of the theoretical uncertainties entering the Higgs rate fits. Both the Bayesian and frequentist approaches are systematically analysed in a unified formalism. We present analytical expressions for the marginal likelihoods, useful to implement simultaneously the experimental and theoretical uncertainties. We review the various origins of the theoretical errors (QCD, EFT, PDF, production mode contamination…). All these individual uncertainties are thoroughly combined with the help of moment-based considerations. The theoretical correlations among Higgs detection channels appear to affect the location and size of the best-fit regions in the space of Higgs couplings. We discuss the recurrent question of the shape of the prior distributions for the individual theoretical errors and find that a nearly Gaussian prior arises from the error combinations. We also develop the bias approach, which is an alternative to marginalisation providing more conservative results. The statistical framework to apply the bias principle is introduced and two realisations of the bias are proposed. Finally, depending on the statistical treatment, the Standard Model prediction for the Higgs signal strengths is found to lie within either the 68% or 95% confidence level region obtained from the latest analyses of the 7 and 8 TeV LHC datasets.

  11. Retrospective correction of bias in diffusion tensor imaging arising from coil combination mode.

    Science.gov (United States)

    Sakaie, Ken; Lowe, Mark

    2017-04-01

    To quantify and retrospectively correct for systematic differences in diffusion tensor imaging (DTI) measurements due to differences in coil combination mode. Multi-channel coils are now standard among MRI systems. There are several options for combining signal from multiple coils during image reconstruction, including sum-of-squares (SOS) and adaptive combine (AC). This contribution examines the bias between SOS- and AC-derived measures of tissue microstructure and a strategy for limiting that bias. Five healthy subjects were scanned under an institutional review board-approved protocol. Each set of raw image data was reconstructed twice-once with SOS and once with AC. The diffusion tensor was calculated from SOS- and AC-derived data by two algorithms-standard log-linear least squares and an approach that accounts for the impact of coil combination on signal statistics. Systematic differences between SOS and AC in terms of tissue microstructure (axial diffusivity, radial diffusivity, mean diffusivity and fractional anisotropy) were evaluated on a voxel-by-voxel basis. SOS-based tissue microstructure values are systematically lower than AC-based measures throughout the brain in each subject when using the standard tensor calculation method. The difference between SOS and AC can be virtually eliminated by taking into account the signal statistics associated with coil combination. The impact of coil combination mode on diffusion tensor-based measures of tissue microstructure is statistically significant but can be corrected retrospectively. The ability to do so is expected to facilitate pooling of data among imaging protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Statistical competencies for medical research learners: What is fundamental?

    Science.gov (United States)

    Enders, Felicity T; Lindsell, Christopher J; Welty, Leah J; Benn, Emma K T; Perkins, Susan M; Mayo, Matthew S; Rahbar, Mohammad H; Kidwell, Kelley M; Thurston, Sally W; Spratt, Heidi; Grambow, Steven C; Larson, Joseph; Carter, Rickey E; Pollock, Brad H; Oster, Robert A

    2017-06-01

    It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from 'fundamental' (necessary for all) to 'specialized' (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized. We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by 'fundamental' and 'specialized.' There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one's own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%). We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.

  13. Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations.

    Science.gov (United States)

    Lubow, Bruce C; Ransom, Jason I

    2016-01-01

    Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs.

  14. A Hybrid Framework to Bias Correct and Empirically Downscale Daily Temperature and Precipitation from Regional Climate Models

    Science.gov (United States)

    Tan, P.; Abraham, Z.; Winkler, J. A.; Perdinan, P.; Zhong, S. S.; Liszewska, M.

    2013-12-01

    Bias correction and statistical downscaling are widely used approaches for postprocessing climate simulations generated by global and/or regional climate models. The skills of these approaches are typically assessed in terms of their ability to reproduce historical climate conditions as well as the plausibility and consistency of the derived statistical indicators needed by end users. Current bias correction and downscaling approaches often do not adequately satisfy the two criteria of accurate prediction and unbiased estimation. To overcome this limitation, a hybrid regression framework was developed to both minimize prediction errors and preserve the distributional characteristics of climate observations. Specifically, the framework couples the loss functions of standard (linear or nonlinear) regression methods with a regularization term that penalizes for discrepancies between the predicted and observed distributions. The proposed framework can also be extended to generate physically-consistent outputs across multiple response variables, and to incorporate both reanalysis-driven and GCM-driven RCM outputs into a unified learning framework. The effectiveness of the framework is demonstrated using daily temperature and precipitation simulations from the North American Regional Climate Change Program (NARCCAP) . The accuracy of the framework is comparable to standard regression methods, but, unlike the standard regression methods, the proposed framework is able to preserve many of the distribution properties of the response variables, akin to bias correction approaches such as quantile mapping and bivariate geometric quantile mapping.

  15. Sampler bias -- Phase 1

    International Nuclear Information System (INIS)

    Blanchard, R.J.

    1995-01-01

    This documents Phase 1 determinations on sampler induced bias for four sampler types used in tank characterization. Each sampler, grab sampler or bottle-on-a-string, auger sampler, sludge sampler and universal sampler, is briefly discussed and their physical limits noted. Phase 2 of this document will define additional testing and analysis to further define Sampler Bias

  16. Statistical model selection with “Big Data”

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2015-12-01

    Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

  17. Python for probability, statistics, and machine learning

    CERN Document Server

    Unpingco, José

    2016-01-01

    This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowl...

  18. Active Contour Driven by Local Region Statistics and Maximum A Posteriori Probability for Medical Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoliang Jiang

    2014-01-01

    Full Text Available This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are simultaneously achieved by minimizing the level set formulation. Experimental results demonstrate desirable performance of the proposed method for different medical images with weak boundaries and noise.

  19. Negativity Bias in Dangerous Drivers.

    Directory of Open Access Journals (Sweden)

    Jing Chai

    Full Text Available The behavioral and cognitive characteristics of dangerous drivers differ significantly from those of safe drivers. However, differences in emotional information processing have seldom been investigated. Previous studies have revealed that drivers with higher anger/anxiety trait scores are more likely to be involved in crashes and that individuals with higher anger traits exhibit stronger negativity biases when processing emotions compared with control groups. However, researchers have not explored the relationship between emotional information processing and driving behavior. In this study, we examined the emotional information processing differences between dangerous drivers and safe drivers. Thirty-eight non-professional drivers were divided into two groups according to the penalty points that they had accrued for traffic violations: 15 drivers with 6 or more points were included in the dangerous driver group, and 23 drivers with 3 or fewer points were included in the safe driver group. The emotional Stroop task was used to measure negativity biases, and both behavioral and electroencephalograph data were recorded. The behavioral results revealed stronger negativity biases in the dangerous drivers than in the safe drivers. The bias score was correlated with self-reported dangerous driving behavior. Drivers with strong negativity biases reported having been involved in mores crashes compared with the less-biased drivers. The event-related potentials (ERPs revealed that the dangerous drivers exhibited reduced P3 components when responding to negative stimuli, suggesting decreased inhibitory control of information that is task-irrelevant but emotionally salient. The influence of negativity bias provides one possible explanation of the effects of individual differences on dangerous driving behavior and traffic crashes.

  20. Religious Attitudes and Home Bias

    OpenAIRE

    C. Reggiani; G. Rossini

    2008-01-01

    Home bias affects trade in goods, services and financial assets. It is mostly generated by "natural" trade barriers. Among these dividers we may list many behavioral and sociological factors, such as status quo biases and a few kind of ‘embeddedness’. Unfortunately these factors are difficult to measure. An important part of ‘embeddedness’ may be related to religious attitudes. Is there any relation between economic home bias and religious attitudes at the individual tier? Our aim is to provi...

  1. Statistical issues in the parton distribution analysis of the Tevatron jet data

    International Nuclear Information System (INIS)

    Alekhin, S.; Bluemlein, J.; Moch, S.O.; Hamburg Univ.

    2012-11-01

    We analyse a tension between the D0 and CDF inclusive jet data and the perturbative QCD calculations, which are based on the ABKM09 and ABM11 parton distribution functions (PDFs) within the nuisance parameter framework. Particular attention is paid on the uncertainties in the nuisance parameters due to the data fluctuations and the PDF errors. We show that with account of these uncertainties the nuisance parameters do not demonstrate a statistically significant excess. A statistical bias of the estimator based on the nuisance parameters is also discussed.

  2. Placebo effect studies are susceptible to response bias and to other types of biases

    DEFF Research Database (Denmark)

    Hróbjartsson, Asbjørn; Kaptchuk, Ted J; Miller, Franklin G

    2011-01-01

    Investigations of the effect of placebo are often challenging to conduct and interpret. The history of placebo shows that assessment of its clinical significance has a real potential to be biased. We analyze and discuss typical types of bias in studies on placebo....

  3. A Comparison of attentional biases and memory biases in social phobia and major depression

    NARCIS (Netherlands)

    Rinck, M.; Becker, E.S.

    2005-01-01

    Cognitive processes play an important role in the etiology and maintenance of anxiety and depression. Current theories differ, however, in their predictions regarding the occurrence of attentional biases and memory biases in depression and anxiety. To allow for a systematic comparison of disorders

  4. Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty.

    Science.gov (United States)

    Lash, Timothy L

    2007-11-26

    The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a qualitative description of study limitations. The latter approach is

  5. Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty

    Directory of Open Access Journals (Sweden)

    Lash Timothy L

    2007-11-01

    Full Text Available Abstract Background The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. Methods For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. Results The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Conclusion Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a

  6. Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling.

    Science.gov (United States)

    Biro, Peter A

    2013-02-01

    Sampling animals from the wild for study is something nearly every biologist has done, but despite our best efforts to obtain random samples of animals, 'hidden' trait biases may still exist. For example, consistent behavioral traits can affect trappability/catchability, independent of obvious factors such as size and gender, and these traits are often correlated with other repeatable physiological and/or life history traits. If so, systematic sampling bias may exist for any of these traits. The extent to which this is a problem, of course, depends on the magnitude of bias, which is presently unknown because the underlying trait distributions in populations are usually unknown, or unknowable. Indeed, our present knowledge about sampling bias comes from samples (not complete population censuses), which can possess bias to begin with. I had the unique opportunity to create naturalized populations of fish by seeding each of four small fishless lakes with equal densities of slow-, intermediate-, and fast-growing fish. Using sampling methods that are not size-selective, I observed that fast-growing fish were up to two-times more likely to be sampled than slower-growing fish. This indicates substantial and systematic bias with respect to an important life history trait (growth rate). If correlations between behavioral, physiological and life-history traits are as widespread as the literature suggests, then many animal samples may be systematically biased with respect to these traits (e.g., when collecting animals for laboratory use), and affect our inferences about population structure and abundance. I conclude with a discussion on ways to minimize sampling bias for particular physiological/behavioral/life-history types within animal populations.

  7. Bias correction method for climate change impact assessment at a basin scale

    Science.gov (United States)

    Nyunt, C.; Jaranilla-sanchez, P. A.; Yamamoto, A.; Nemoto, T.; Kitsuregawa, M.; Koike, T.

    2012-12-01

    Climate change impact studies are mainly based on the general circulation models GCM and these studies play an important role to define suitable adaptation strategies for resilient environment in a basin scale management. For this purpose, this study summarized how to select appropriate GCM to decrease the certain uncertainty amount in analysis. This was applied to the Pampanga, Angat and Kaliwa rivers in Luzon Island, the main island of Philippine and these three river basins play important roles in irrigation water supply, municipal water source for Metro Manila. According to the GCM scores of both seasonal evolution of Asia summer monsoon and spatial correlation and root mean squared error of atmospheric variables over the region, finally six GCM is chosen. Next, we develop a complete, efficient and comprehensive statistical bias correction scheme covering extremes events, normal rainfall and frequency of dry period. Due to the coarse resolution and parameterization scheme of GCM, extreme rainfall underestimation, too many rain days with low intensity and poor representation of local seasonality have been known as bias of GCM. Extreme rainfall has unusual characteristics and it should be focused specifically. Estimated maximum extreme rainfall is crucial for planning and design of infrastructures in river basin. Developing countries have limited technical, financial and management resources for implementing adaptation measures and they need detailed information of drought and flood for near future. Traditionally, the analysis of extreme has been examined using annual maximum series (AMS) adjusted to a Gumbel or Lognormal distribution. The drawback is the loss of the second, third etc, largest rainfall. Another approach is partial duration series (PDS) constructed using the values above a selected threshold and permit more than one event per year. The generalized Pareto distribution (GPD) has been used to model PDS and it is the series of excess over a threshold

  8. Selection bias and subject refusal in a cluster-randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Rochelle Yang

    2017-07-01

    Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1

  9. Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

    Science.gov (United States)

    Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

    2016-01-01

    Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

  10. Atomoxetine effects on attentional bias to drug-related cues in cocaine dependent individuals.

    Science.gov (United States)

    Passamonti, Luca; Luijten, M; Ziauddeen, H; Coyle-Gilchrist, I T S; Rittman, T; Brain, S A E; Regenthal, R; Franken, I H A; Sahakian, B J; Bullmore, E T; Robbins, T W; Ersche, K D

    2017-08-01

    Biased attention towards drug-related cues and reduced inhibitory control over the regulation of drug-intake characterize drug addiction. The noradrenaline system has been critically implicated in both attentional and response inhibitory processes and is directly affected by drugs such as cocaine. We examined the potentially beneficial effects of the noradrenaline reuptake inhibitor atomoxetine in improving cognitive control during two tasks that used cocaine- and non-cocaine-related stimuli. A double-blind, placebo-controlled, and cross-over psycho-pharmacological design was employed. A single oral dose of atomoxetine (40 mg) was administered to 28 cocaine-dependent individuals (CDIs) and 28 healthy controls. All participants performed a pictorial attentional bias task involving both cocaine- and non-cocaine-related pictures as well as a verbal go/no-go task composed of cocaine- and food-related words. As expected, CDIs showed attentional bias to cocaine-related cues whilst controls did not. More importantly, however, atomoxetine, relative to placebo, significantly attenuated attentional bias in CDIs (F 26  = 6.73, P = 0.01). During the go/no-go task, there was a treatment × trial × group interaction, although this finding only showed a trend towards statistical significance (F 26  = 3.38, P = 0.07). Our findings suggest that atomoxetine reduces attentional bias to drug-related cues in CDIs. This may result from atomoxetine's modulation of the balance between tonic/phasic activity in the locus coeruleus and the possibly parallel enhancement of noradrenergic neurotransmission within the prefrontal cortex. Studying how cognitive enhancers such as atomoxetine influence key neurocognitive indices in cocaine addiction may help to develop reliable biomarkers for patient stratification in future clinical trials.

  11. bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests.

    Science.gov (United States)

    To Duc, Khanh

    2017-11-18

    Receiver operating characteristic (ROC) surface analysis is usually employed to assess the accuracy of a medical diagnostic test when there are three ordered disease status (e.g. non-diseased, intermediate, diseased). In practice, verification bias can occur due to missingness of the true disease status and can lead to a distorted conclusion on diagnostic accuracy. In such situations, bias-corrected inference tools are required. This paper introduce an R package, named bcROCsurface, which provides utility functions for verification bias-corrected ROC surface analysis. The shiny web application of the correction for verification bias in estimation of the ROC surface analysis is also developed. bcROCsurface may become an important tool for the statistical evaluation of three-class diagnostic markers in presence of verification bias. The R package, readme and example data are available on CRAN. The web interface enables users less familiar with R to evaluate the accuracy of diagnostic tests, and can be found at http://khanhtoduc.shinyapps.io/bcROCsurface_shiny/ .

  12. Domain wall engineering through exchange bias

    International Nuclear Information System (INIS)

    Albisetti, E.; Petti, D.

    2016-01-01

    The control of the structure and position of magnetic domain walls is at the basis of the development of different magnetic devices and architectures. Several nanofabrication techniques have been proposed to geometrically confine and shape domain wall structures; however, a fine tuning of the position and micromagnetic configuration is hardly achieved, especially in continuous films. This work shows that, by controlling the unidirectional anisotropy of a continuous ferromagnetic film through exchange bias, domain walls whose spin arrangement is generally not favored by dipolar and exchange interactions can be created. Micromagnetic simulations reveal that the domain wall width, position and profile can be tuned by establishing an abrupt change in the direction and magnitude of the exchange bias field set in the system. - Highlights: • Micromagnetic simulations study domain walls in exchange biased thin films. • Novel domain wall configurations can be stabilized via exchange bias. • Domain walls nucleate at the boundary of regions with different exchange bias. • Domain wall width and spin profile are controlled by tuning the exchange bias.

  13. Lensing corrections to the E {sub g} ( z ) statistics from large scale structure

    Energy Technology Data Exchange (ETDEWEB)

    Dizgah, Azadeh Moradinezhad; Durrer, Ruth, E-mail: Azadeh.Moradinezhad@unige.ch, E-mail: Ruth.Durrer@unige.ch [Department of Theoretical Physics and Center for Astroparticle Physics, University of Geneva, 24 quai E. Ansermet, CH-1211 Geneva 4 (Switzerland)

    2016-09-01

    We study the impact of the often neglected lensing contribution to galaxy number counts on the E {sub g} statistics which is used to constrain deviations from GR. This contribution affects both the galaxy-galaxy and the convergence-galaxy spectra, while it is larger for the latter. At higher redshifts probed by upcoming surveys, for instance at z = 1.5, neglecting this term induces an error of (25–40)% in the spectra and therefore on the E {sub g} statistics which is constructed from the combination of the two. Moreover, including it, renders the E {sub g} statistics scale and bias-dependent and hence puts into question its very objective.

  14. How we categorize objects is related to how we remember them: The shape bias as a memory bias.

    Science.gov (United States)

    Vlach, Haley A

    2016-12-01

    The "shape bias" describes the phenomenon that, after a certain point in development, children and adults generalize object categories based on shape to a greater degree than other perceptual features. The focus of research on the shape bias has been to examine the types of information that learners attend to in one moment in time. The current work takes a different approach by examining whether learners' categorical biases are related to their retention of information across time. In three experiments, children's (N=72) and adults' (N=240) memory performance for features of objects was examined in relation to their categorical biases. The results of these experiments demonstrated that the number of shape matches chosen during the shape bias task significantly predicted shape memory. Moreover, children and adults with a shape bias were more likely to remember the shape of objects than the color and size of objects. Taken together, this work suggests that the development of a shape bias may engender better memory for shape information. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Is bias in the eye of the beholder? A vignette study to assess recognition of cognitive biases in clinical case workups.

    Science.gov (United States)

    Zwaan, Laura; Monteiro, Sandra; Sherbino, Jonathan; Ilgen, Jonathan; Howey, Betty; Norman, Geoffrey

    2017-02-01

    Many authors have implicated cognitive biases as a primary cause of diagnostic error. If this is so, then physicians already familiar with common cognitive biases should consistently identify biases present in a clinical workup. The aim of this paper is to determine whether physicians agree on the presence or absence of particular biases in a clinical case workup and how case outcome knowledge affects bias identification. We conducted a web survey of 37 physicians. Each participant read eight cases and listed which biases were present from a list provided. In half the cases the outcome implied a correct diagnosis; in the other half, it implied an incorrect diagnosis. We compared the number of biases identified when the outcome implied a correct or incorrect primary diagnosis. Additionally, the agreement among participants about presence or absence of specific biases was assessed. When the case outcome implied a correct diagnosis, an average of 1.75 cognitive biases were reported; when incorrect, 3.45 biases (F=71.3, p<0.00001). Individual biases were reported from 73% to 125% more often when an incorrect diagnosis was implied. There was no agreement on presence or absence of individual biases, with κ ranging from 0.000 to 0.044. Individual physicians are unable to agree on the presence or absence of individual cognitive biases. Their judgements are heavily influenced by hindsight bias; when the outcome implies a diagnostic error, twice as many biases are identified. The results present challenges for current error reduction strategies based on identification of cognitive biases. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  16. PHYSICS OF NON-GAUSSIAN FIELDS AND THE COSMOLOGICAL GENUS STATISTIC

    International Nuclear Information System (INIS)

    James, J. Berian

    2012-01-01

    We report a technique to calculate the impact of distinct physical processes inducing non-Gaussianity on the cosmological density field. A natural decomposition of the cosmic genus statistic into an orthogonal polynomial sequence allows complete expression of the scale-dependent evolution of the topology of large-scale structure, in which effects including galaxy bias, nonlinear gravitational evolution, and primordial non-Gaussianity may be delineated. The relationship of this decomposition to previous methods for analyzing the genus statistic is briefly considered and the following applications are made: (1) the expression of certain systematics affecting topological measurements, (2) the quantification of broad deformations from Gaussianity that appear in the genus statistic as measured in the Horizon Run simulation, and (3) the study of the evolution of the genus curve for simulations with primordial non-Gaussianity. These advances improve the treatment of flux-limited galaxy catalogs for use with this measurement and further the use of the genus statistic as a tool for exploring non-Gaussianity.

  17. Assessing bias against overweight individuals among nursing and psychology students: an implicit association test.

    Science.gov (United States)

    Waller, Tabitha; Lampman, Claudia; Lupfer-Johnson, Gwen

    2012-12-01

    To determine the implicit or unconscious attitudes of Nursing and Psychology majors towards overweight individuals in medical and non-medical contexts. Obesity is a leading health concern today, which impacts both physical and psychological health. Overweight individuals confront social biases in many aspects of their lives including health care. Examining the views of Nursing and Psychology students may reveal implicit attitudes towards overweight individuals that may lead to prejudiced behaviours. A mixed design experiment with one between-subjects variable (student major: Nursing or Psychology) and one within-subjects variable (condition: congruent or incongruent) was used to assess implicit attitudes in two convenience samples of Nursing and Psychology students. A computerised implicit association test was used to determine implicit attitudes towards overweight individuals in medical and non-medical contexts. A total of 90 students from Nursing (n= 45) and Psychology (n = 45) were recruited to complete an implicit association test. Reaction times in milliseconds between the congruent trials (stereotype consistent) and incongruent trials (stereotype inconsistent) were compared with determine adherence to social stereotypes or weight bias. A statistically significant implicit bias towards overweight individuals was detected in both subject groups and in both target settings (medical vs. non-medical). Stronger weight bias was found when the stimulus targets were female than male. Findings from this study expand understanding of the implicit attitudes and social biases of Nursing and Psychology students. The views held by these future healthcare professionals may negatively impact patient care. Providing education and support to overweight individuals is central to Nursing practice in a society struggling to manage obesity. Negative stereotypes or beliefs about these individuals may result in poor patient care. Therefore, nurses and other healthcare professionals

  18. AC bias operation of the perpendicular biased ferrite tuned cavity for the TRIUMF KAON Factory booster synchrotron

    International Nuclear Information System (INIS)

    Poirier, R.L.; Enegren, T.A.; Enchevich, I.B.

    1991-05-01

    The RF cavity for the booster synchrotron requires a frequency swing from 46 MHz at a repetition rate of 50 Hz and a maximum accelerating gap voltage of 65 kV. A DC biased prototype cavity built at LANL using perpendicular-biased yttrium-garnet ferrites, rather than the more conventional parallel-biased NiZn ferrites, has now undergone major reconstruction at TRIUMF for AC bias operation. RF signal level measurements have shown that the frequency swing at a repetition rate of 50 Hz can be accomplished and still handle the eddy current losses in the cavity structures with minimal effect on the magnetizing field. The prototype cavity is now undergoing high power RF tests with full power AC bias operation. The results of these tests and operational experience is reported. (Author) ref., 6 figs

  19. A Statistical Model for Misreported Binary Outcomes in Clustered RCTs of Education Interventions

    Science.gov (United States)

    Schochet, Peter Z.

    2013-01-01

    In education randomized control trials (RCTs), the misreporting of student outcome data could lead to biased estimates of average treatment effects (ATEs) and their standard errors. This article discusses a statistical model that adjusts for misreported binary outcomes for two-level, school-based RCTs, where it is assumed that misreporting could…

  20. OSSOS. VI. Striking Biases in the Detection of Large Semimajor Axis Trans-Neptunian Objects

    Science.gov (United States)

    Shankman, Cory; Kavelaars, J. J.; Bannister, Michele T.; Gladman, Brett J.; Lawler, Samantha M.; Chen, Ying-Tung; Jakubik, Marian; Kaib, Nathan; Alexandersen, Mike; Gwyn, Stephen D. J.; Petit, Jean-Marc; Volk, Kathryn

    2017-08-01

    The accumulating but small set of large semimajor axis trans-Neptunian objects (TNOs) shows an apparent clustering in the orientations of their orbits. This clustering must either be representative of the intrinsic distribution of these TNOs, or else have arisen as a result of observation biases and/or statistically expected variations for such a small set of detected objects. The clustered TNOs were detected across different and independent surveys, which has led to claims that the detections are therefore free of observational bias. This apparent clustering has led to the so-called “Planet 9” hypothesis that a super-Earth currently resides in the distant solar system and causes this clustering. The Outer Solar System Origins Survey (OSSOS) is a large program that ran on the Canada–France–Hawaii Telescope from 2013 to 2017, discovering more than 800 new TNOs. One of the primary design goals of OSSOS was the careful determination of observational biases that would manifest within the detected sample. We demonstrate the striking and non-intuitive biases that exist for the detection of TNOs with large semimajor axes. The eight large semimajor axis OSSOS detections are an independent data set, of comparable size to the conglomerate samples used in previous studies. We conclude that the orbital distribution of the OSSOS sample is consistent with being detected from a uniform underlying angular distribution.

  1. delta-biased Josephson tunnel junctions

    DEFF Research Database (Denmark)

    Monaco, R.; Mygind, Jesper; Koshelet, V.

    2010-01-01

    Abstract: The behavior of a long Josephson tunnel junction drastically depends on the distribution of the dc bias current. We investigate the case in which the bias current is fed in the central point of a one-dimensional junction. Such junction configuration has been recently used to detect...... the persistent currents circulating in a superconducting loop. Analytical and numerical results indicate that the presence of fractional vortices leads to remarkable differences from the conventional case of uniformly distributed dc bias current. The theoretical findings are supported by detailed measurements...

  2. Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean.

    Science.gov (United States)

    Marcos, Raül; Llasat, Ma Carmen; Quintana-Seguí, Pere; Turco, Marco

    2018-01-01

    In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Effect of negative bias voltage on CrN films deposited by arc ion plating. I. Macroparticles filtration and film-growth characteristics

    International Nuclear Information System (INIS)

    Wang Qimin; Kim, Kwang Ho

    2008-01-01

    Chromium nitride (CrN) films were deposited on Si wafers by arc ion plating (AIP) at various negative bias voltages and several groups of N 2 /Ar gas flux ratios and chamber gas pressures. The authors systematically investigated the influence of negative bias voltage on the synthesis, composition, microstructure, and properties of the AIP CrN films. In this part (Part I), the investigations were mainly focused on the macroparticle distributions and film-growth characteristics. The results showed that macroparticle densities on the film surfaces decreased greatly by applying negative bias voltage, which can be affected by partial pressure of N 2 and Ar gases. From the statistical analysis of the experimental results, they proposed a new hybrid mechanism of ion bombardment and electrical repulsion. Also, the growth of the AIP CrN films was greatly altered by applying negative bias voltage. By increasing the bias voltage, the film surfaces became much smoother and the films evolved from apparent columnar microstructures to an equiaxed microstructure. The impinging high-energy Cr ions accelerated by negative bias voltages were deemed the inherent reason for the evolution of growth characteristics

  4. (Biased) Grading of Students’ Performance: Students’ Names, Performance Level, and Implicit Attitudes

    Science.gov (United States)

    Bonefeld, Meike; Dickhäuser, Oliver

    2018-01-01

    Biases in pre-service teachers’ evaluations of students’ performance may arise due to stereotypes (e.g., the assumption that students with a migrant background have lower potential). This study examines the effects of a migrant background, performance level, and implicit attitudes toward individuals with a migrant background on performance assessment (assigned grades and number of errors counted in a dictation). Pre-service teachers (N = 203) graded the performance of a student who appeared to have a migrant background statistically significantly worse than that of a student without a migrant background. The differences were more pronounced when the performance level was low and when the pre-service teachers held relatively positive implicit attitudes toward individuals with a migrant background. Interestingly, only performance level had an effect on the number of counted errors. Our results support the assumption that pre-service teachers exhibit bias when grading students with a migrant background in a third-grade level dictation assignment. PMID:29867618

  5. (Biased Grading of Students’ Performance: Students’ Names, Performance Level, and Implicit Attitudes

    Directory of Open Access Journals (Sweden)

    Meike Bonefeld

    2018-05-01

    Full Text Available Biases in pre-service teachers’ evaluations of students’ performance may arise due to stereotypes (e.g., the assumption that students with a migrant background have lower potential. This study examines the effects of a migrant background, performance level, and implicit attitudes toward individuals with a migrant background on performance assessment (assigned grades and number of errors counted in a dictation. Pre-service teachers (N = 203 graded the performance of a student who appeared to have a migrant background statistically significantly worse than that of a student without a migrant background. The differences were more pronounced when the performance level was low and when the pre-service teachers held relatively positive implicit attitudes toward individuals with a migrant background. Interestingly, only performance level had an effect on the number of counted errors. Our results support the assumption that pre-service teachers exhibit bias when grading students with a migrant background in a third-grade level dictation assignment.

  6. The immitigable nature of assembly bias: the impact of halo definition on assembly bias

    Science.gov (United States)

    Villarreal, Antonio S.; Zentner, Andrew R.; Mao, Yao-Yuan; Purcell, Chris W.; van den Bosch, Frank C.; Diemer, Benedikt; Lange, Johannes U.; Wang, Kuan; Campbell, Duncan

    2017-11-01

    Dark matter halo clustering depends not only on halo mass, but also on other properties such as concentration and shape. This phenomenon is known broadly as assembly bias. We explore the dependence of assembly bias on halo definition, parametrized by spherical overdensity parameter, Δ. We summarize the strength of concentration-, shape-, and spin-dependent halo clustering as a function of halo mass and halo definition. Concentration-dependent clustering depends strongly on mass at all Δ. For conventional halo definitions (Δ ∼ 200 - 600 m), concentration-dependent clustering at low mass is driven by a population of haloes that is altered through interactions with neighbouring haloes. Concentration-dependent clustering can be greatly reduced through a mass-dependent halo definition with Δ ∼ 20 - 40 m for haloes with M200 m ≲ 1012 h-1M⊙. Smaller Δ implies larger radii and mitigates assembly bias at low mass by subsuming altered, so-called backsplash haloes into now larger host haloes. At higher masses (M200 m ≳ 1013 h-1M⊙) larger overdensities, Δ ≳ 600 m, are necessary. Shape- and spin-dependent clustering are significant for all halo definitions that we explore and exhibit a relatively weaker mass dependence. Generally, both the strength and the sense of assembly bias depend on halo definition, varying significantly even among common definitions. We identify no halo definition that mitigates all manifestations of assembly bias. A halo definition that mitigates assembly bias based on one halo property (e.g. concentration) must be mass dependent. The halo definitions that best mitigate concentration-dependent halo clustering do not coincide with the expected average splashback radii at fixed halo mass.

  7. Is racial bias malleable? Whites' lay theories of racial bias predict divergent strategies for interracial interactions.

    Science.gov (United States)

    Neel, Rebecca; Shapiro, Jenessa R

    2012-07-01

    How do Whites approach interracial interactions? We argue that a previously unexamined factor-beliefs about the malleability of racial bias-guides Whites' strategies for difficult interracial interactions. We predicted and found that those who believe racial bias is malleable favor learning-oriented strategies such as taking the other person's perspective and trying to learn why an interaction is challenging, whereas those who believe racial bias is fixed favor performance-oriented strategies such as overcompensating in the interaction and trying to end the interaction as quickly as possible. Four studies support these predictions. Whether measured (Studies 1, 3, and 4) or manipulated (Study 2), beliefs that racial bias is fixed versus malleable yielded these divergent strategies for difficult interracial interactions. Furthermore, beliefs about the malleability of racial bias are distinct from related constructs (e.g., prejudice and motivations to respond without prejudice; Studies 1, 3, and 4) and influence self-reported (Studies 1-3) and actual (Study 4) strategies in imagined (Studies 1-2) and real (Studies 3-4) interracial interactions. Together, these findings demonstrate that beliefs about the malleability of racial bias influence Whites' approaches to and strategies within interracial interactions. PsycINFO Database Record (c) 2012 APA, all rights reserved

  8. Attention bias to emotional information in children as a function of maternal emotional disorders and maternal attention biases.

    Science.gov (United States)

    Waters, Allison M; Forrest, Kylee; Peters, Rosie-Mae; Bradley, Brendan P; Mogg, Karin

    2015-03-01

    Children of parents with emotional disorders have an increased risk for developing anxiety and depressive disorders. Yet the mechanisms that contribute to this increased risk are poorly understood. The present study aimed to examine attention biases in children as a function of maternal lifetime emotional disorders and maternal attention biases. There were 134 participants, including 38 high-risk children, and their mothers who had lifetime emotional disorders; and 29 low-risk children, and their mothers without lifetime emotional disorders. Mothers and children completed a visual probe task with emotional face pairs presented for 500 ms. Attention bias in children did not significantly differ solely as a function of whether or not their mothers had lifetime emotional disorders. However, attention bias in high-risk children was significantly related to their mothers' attention bias. Specifically, children of mothers with lifetime emotional disorders showed a greater negative attention bias if their mothers had a greater tendency to direct attention away from positive information. This study was cross-sectional in nature, and therefore unable to assess long-term predictive effects. Also, just one exposure duration of 500 ms was utilised. Attention bias for negative information is greater in offspring of mothers who have lifetime emotional disorders and a reduced positive bias, which could be a risk marker for the development of emotional disorders in children.

  9. Weight bias internalization in treatment-seeking overweight adults: Psychometric validation and associations with self-esteem, body image, and mood symptoms.

    Science.gov (United States)

    Durso, Laura E; Latner, Janet D; Ciao, Anna C

    2016-04-01

    Internalized weight bias has been previously associated with impairments in eating behaviors, body image, and psychological functioning. The present study explored the psychological correlates and psychometric properties of the Weight Bias Internalization Scale (WBIS) among overweight adults enrolled in a behavioral weight loss program. Questionnaires assessing internalized weight bias, anti-fat attitudes, self-esteem, body image concern, and mood symptoms were administered to 90 obese or overweight men and women between the ages of 21 and 73. Reliability statistics suggested revisions to the WBIS. The resulting 9-item scale was shown to be positively associated with body image concern, depressive symptoms, and stress, and negatively associated with self-esteem. Multiple linear regression models demonstrated that WBIS scores were significant and independent predictors of body image concern, self-esteem, and depressive symptoms. These results support the use of the revised 9-item WBIS in treatment-seeking samples as a reliable and valid measure of internalized weight bias. Copyright © 2016. Published by Elsevier Ltd.

  10. Detailed statistical analysis plan for the target temperature management after out-of-hospital cardiac arrest trial

    DEFF Research Database (Denmark)

    Nielsen, Niklas; Winkel, Per; Cronberg, Tobias

    2013-01-01

    Animal experimental studies and previous randomized trials suggest an improvement in mortality and neurological function with temperature regulation to hypothermia after cardiac arrest. According to a systematic review, previous trials were small, had a risk of bias, evaluated select populations......, and did not treat hyperthermia in the control groups. The optimal target temperature management (TTM) strategy is not known. To prevent outcome reporting bias, selective reporting and data-driven results, we present the a priori defined detailed statistical analysis plan as an update to the previously...

  11. Does neurocognitive function affect cognitive bias toward an emotional stimulus? Association between general attentional ability and attentional bias toward threat

    Directory of Open Access Journals (Sweden)

    Yuko eHakamata

    2014-08-01

    Full Text Available Background: Although poorer cognitive performance has been found to be associated with anxiety, it remains unclear whether neurocognitive function affects biased cognitive processing toward emotional information. We investigated whether general cognitive function evaluated with a standard neuropsychological test predicts biased cognition, focusing on attentional bias toward threat.Methods: One hundred and five healthy young adults completed a dot-probe task measuring attentional bias and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS measuring general cognitive function, which consists of five domains: immediate memory, visuospatial/constructional, language, attention, and delayed memory. Stepwise multiple regression analysis was performed to examine the relationships between attentional bias and cognitive function. Results: The attentional domain was the best predictor of attentional bias toward threat (β = -0.26, p = 0.006. Within the attentional domain, digit symbol coding was negatively correlated with attentional bias (r = -0.28, p = 0.005.Conclusions: The present study provides the first evidence that general attentional ability, which was assessed with a standard neuropsychological test, affects attentional bias toward threatening information. Individual cognitive profiles might be important for the measurement and modification of cognitive biases.

  12. Cognitive Biases and Nonverbal Cue Availability in Detecting Deception

    Science.gov (United States)

    Burgoon, Judee K.; Blair, J. Pete; Strom, Renee E.

    2008-01-01

    In potentially deceptive situations, people rely on mental shortcuts to help process information. These heuristic judgments are often biased and result in inaccurate assessments of sender veracity. Four such biases--truth bias, visual bias, demeanor bias, and expectancy violation bias--were examined in a judgment experiment that varied nonverbal…

  13. The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.

    Science.gov (United States)

    Kendal, Jeremy; Giraldeau, Luc-Alain; Laland, Kevin

    2009-09-21

    Humans and other animals do not use social learning indiscriminately, rather, natural selection has favoured the evolution of social learning rules that make selective use of social learning to acquire relevant information in a changing environment. We present a gene-culture coevolutionary analysis of a small selection of such rules (unbiased social learning, payoff-biased social learning and frequency-dependent biased social learning, including conformism and anti-conformism) in a population of asocial learners where the environment is subject to a constant probability of change to a novel state. We define conditions under which each rule evolves to a genetically polymorphic equilibrium. We find that payoff-biased social learning may evolve under high levels of environmental variation if the fitness benefit associated with the acquired behaviour is either high or low but not of intermediate value. In contrast, both conformist and anti-conformist biases can become fixed when environment variation is low, whereupon the mean fitness in the population is higher than for a population of asocial learners. Our examination of the population dynamics reveals stable limit cycles under conformist and anti-conformist biases and some highly complex dynamics including chaos. Anti-conformists can out-compete conformists when conditions favour a low equilibrium frequency of the learned behaviour. We conclude that evolution, punctuated by the repeated successful invasion of different social learning rules, should continuously favour a reduction in the equilibrium frequency of asocial learning, and propose that, among competing social learning rules, the dominant rule will be the one that can persist with the lowest frequency of asocial learning.

  14. The Systematic Bias of Ingestible Core Temperature Sensors Requires a Correction by Linear Regression

    Directory of Open Access Journals (Sweden)

    Andrew P. Hunt

    2017-04-01

    Full Text Available An accurate measure of core body temperature is critical for monitoring individuals, groups and teams undertaking physical activity in situations of high heat stress or prolonged cold exposure. This study examined the range in systematic bias of ingestible temperature sensors compared to a certified and traceable reference thermometer. A total of 119 ingestible temperature sensors were immersed in a circulated water bath at five water temperatures (TEMP A: 35.12 ± 0.60°C, TEMP B: 37.33 ± 0.56°C, TEMP C: 39.48 ± 0.73°C, TEMP D: 41.58 ± 0.97°C, and TEMP E: 43.47 ± 1.07°C along with a certified traceable reference thermometer. Thirteen sensors (10.9% demonstrated a systematic bias > ±0.1°C, of which 4 (3.3% were > ± 0.5°C. Limits of agreement (95% indicated that systematic bias would likely fall in the range of −0.14 to 0.26°C, highlighting that it is possible for temperatures measured between sensors to differ by more than 0.4°C. The proportion of sensors with systematic bias > ±0.1°C (10.9% confirms that ingestible temperature sensors require correction to ensure their accuracy. An individualized linear correction achieved a mean systematic bias of 0.00°C, and limits of agreement (95% to 0.00–0.00°C, with 100% of sensors achieving ±0.1°C accuracy. Alternatively, a generalized linear function (Corrected Temperature (°C = 1.00375 × Sensor Temperature (°C − 0.205549, produced as the average slope and intercept of a sub-set of 51 sensors and excluding sensors with accuracy outside ±0.5°C, reduced the systematic bias to < ±0.1°C in 98.4% of the remaining sensors (n = 64. In conclusion, these data show that using an uncalibrated ingestible temperature sensor may provide inaccurate data that still appears to be statistically, physiologically, and clinically meaningful. Correction of sensor temperature to a reference thermometer by linear function eliminates this systematic bias (individualized functions or ensures

  15. Study of the Dependency on Magnetic Field and Bias Voltage of an AC-Biased TES Microcalorimeter

    Science.gov (United States)

    Gottardi, L.; Bruijn, M.; denHartog, R.; Hoevers, H.; deKorte, P.; vanderKuur, J.; Linderman, M.; Adams, J.; Bailey, C.; Bandler, S.; hide

    2012-01-01

    At SRON we are studying the performance of a Goddard Space Flight Center single pixel TES microcalorimeter operated in an AC bias configuration. For x-ray photons at 6 keV the pixel shows an x-ray energy resolution Delta E(sub FWHM) = 3.7 eV, which is about a factor 2 worse than the energy resolution observed in an identical DC-biased pixel. In order to better understand the reasons for this discrepancy we characterized the detector as a function of temperature, bias working point and applied perpendicular magnetic field. A strong periodic dependency of the detector noise on the TES AC bias voltage is measured. We discuss the results in the framework of the recently observed weak-link behaviour of a TES microcalorimeter.

  16. Critical Thinking and Cognitive Bias

    Directory of Open Access Journals (Sweden)

    Jeffrey Maynes

    2015-05-01

    Full Text Available Teaching critical thinking skill is a central pedagogical aim in many courses. These skills, it is hoped, will be both portable (applicable in a wide range of contexts and durable (not forgotten quickly. Yet, both of these virtues are challenged by pervasive and potent cognitive biases, such as motivated reasoning, false consensus bias and hindsight bias. In this paper, I argue that a focus on the development of metacognitive skill shows promise as a means to inculcate debiasing habits in students. Such habits will help students become more critical reasoners. I close with suggestions for implementing this strategy.

  17. Numerical value biases sound localization.

    Science.gov (United States)

    Golob, Edward J; Lewald, Jörg; Getzmann, Stephan; Mock, Jeffrey R

    2017-12-08

    Speech recognition starts with representations of basic acoustic perceptual features and ends by categorizing the sound based on long-term memory for word meaning. However, little is known about whether the reverse pattern of lexical influences on basic perception can occur. We tested for a lexical influence on auditory spatial perception by having subjects make spatial judgments of number stimuli. Four experiments used pointing or left/right 2-alternative forced choice tasks to examine perceptual judgments of sound location as a function of digit magnitude (1-9). The main finding was that for stimuli presented near the median plane there was a linear left-to-right bias for localizing smaller-to-larger numbers. At lateral locations there was a central-eccentric location bias in the pointing task, and either a bias restricted to the smaller numbers (left side) or no significant number bias (right side). Prior number location also biased subsequent number judgments towards the opposite side. Findings support a lexical influence on auditory spatial perception, with a linear mapping near midline and more complex relations at lateral locations. Results may reflect coding of dedicated spatial channels, with two representing lateral positions in each hemispace, and the midline area represented by either their overlap or a separate third channel.

  18. Non-Gaussian halo assembly bias

    International Nuclear Information System (INIS)

    Reid, Beth A.; Verde, Licia; Dolag, Klaus; Matarrese, Sabino; Moscardini, Lauro

    2010-01-01

    The strong dependence of the large-scale dark matter halo bias on the (local) non-Gaussianity parameter, f NL , offers a promising avenue towards constraining primordial non-Gaussianity with large-scale structure surveys. In this paper, we present the first detection of the dependence of the non-Gaussian halo bias on halo formation history using N-body simulations. We also present an analytic derivation of the expected signal based on the extended Press-Schechter formalism. In excellent agreement with our analytic prediction, we find that the halo formation history-dependent contribution to the non-Gaussian halo bias (which we call non-Gaussian halo assembly bias) can be factorized in a form approximately independent of redshift and halo mass. The correction to the non-Gaussian halo bias due to the halo formation history can be as large as 100%, with a suppression of the signal for recently formed halos and enhancement for old halos. This could in principle be a problem for realistic galaxy surveys if observational selection effects were to pick galaxies occupying only recently formed halos. Current semi-analytic galaxy formation models, for example, imply an enhancement in the expected signal of ∼ 23% and ∼ 48% for galaxies at z = 1 selected by stellar mass and star formation rate, respectively

  19. Statistical evaluation and measuring strategy for extremely small line shifts

    International Nuclear Information System (INIS)

    Hansen, P.G.

    1978-01-01

    For a measuring situation limited by counting statistics, but where the level of precision is such that possible systematic errors are a major concern, it is proposed to determine the position of a spectral line from a measured line segment by applying a bias correction to the centre of gravity of the segment. This procedure is statistically highly efficient and not sensitive to small errors in assumptions about the line shape. The counting strategy for an instrument that takes data point by point is also considered. It is shown that an optimum (''two-point'') strategy exists; a scan of the central part of the line is 68% efficient by this standard. (Auth.)

  20. Geographic bias related to geocoding in epidemiologic studies

    Directory of Open Access Journals (Sweden)

    Siadaty Mir

    2005-11-01

    Full Text Available Abstract Background This article describes geographic bias in GIS analyses with unrepresentative data owing to missing geocodes, using as an example a spatial analysis of prostate cancer incidence among whites and African Americans in Virginia, 1990–1999. Statistical tests for clustering were performed and such clusters mapped. The patterns of missing census tract identifiers for the cases were examined by generalized linear regression models. Results The county of residency for all cases was known, and 26,338 (74% of these cases were geocoded successfully to census tracts. Cluster maps showed patterns that appeared markedly different, depending upon whether one used all cases or those geocoded to the census tract. Multivariate regression analysis showed that, in the most rural counties (where the missing data were concentrated, the percent of a county's population over age 64 and with less than a high school education were both independently associated with a higher percent of missing geocodes. Conclusion We found statistically significant pattern differences resulting from spatially non-random differences in geocoding completeness across Virginia. Appropriate interpretation of maps, therefore, requires an understanding of this phenomenon, which we call "cartographic confounding."

  1. A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors.

    Science.gov (United States)

    Zhao, Huaqing; Rebbeck, Timothy R; Mitra, Nandita

    2009-12-01

    Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors. 2009 Wiley-Liss, Inc.

  2. Information bias and lifetime mortality risks of radiation-induced cancer: Low LET radiation

    International Nuclear Information System (INIS)

    Peterson, L.E.; Schull, W.J.; Davis, B.R.; Buffler, P.A.

    1994-04-01

    Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error (σ=0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with risks in RERF Report 11 (Part 2) and the BEIR-V Report. Bias due to DS86 random error typically ranged from -15% to -30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative project model was -37.1% for males and -23.3% for females. Total excess risks of leukemia under the relative projection model were biased -27.1% for males and -43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 65 (DRREF=2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.92%/Sv among females. Leukemia excess risk increased from 0.87%/Sv to 1.10/Sv among males and from 0.73%/Sv to 1.04/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for US nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors

  3. Information bias and lifetime mortality risks of radiation-induced cancer: Low LET radiation

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, L.E.; Schull, W.J.; Davis, B.R. [Texas Univ., Houston, TX (United States). Health Science Center; Buffler, P.A. [California Univ., Berkeley, CA (United States). School of Public Health

    1994-04-01

    Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error ({sigma}=0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with risks in RERF Report 11 (Part 2) and the BEIR-V Report. Bias due to DS86 random error typically ranged from {minus}15% to {minus}30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative project model was {minus}37.1% for males and {minus}23.3% for females. Total excess risks of leukemia under the relative projection model were biased {minus}27.1% for males and {minus}43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 65 (DRREF=2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.92%/Sv among females. Leukemia excess risk increased from 0.87%/Sv to 1.10/Sv among males and from 0.73%/Sv to 1.04/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for US nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors.

  4. Attentional Bias towards Positive Emotion Predicts Stress Resilience.

    Science.gov (United States)

    Thoern, Hanna A; Grueschow, Marcus; Ehlert, Ulrike; Ruff, Christian C; Kleim, Birgit

    2016-01-01

    There is extensive evidence for an association between an attentional bias towards emotionally negative stimuli and vulnerability to stress-related psychopathology. Less is known about whether selective attention towards emotionally positive stimuli relates to mental health and stress resilience. The current study used a modified Dot Probe task to investigate if individual differences in attentional biases towards either happy or angry emotional stimuli, or an interaction between these biases, are related to self-reported trait stress resilience. In a nonclinical sample (N = 43), we indexed attentional biases as individual differences in reaction time for stimuli preceded by either happy or angry (compared to neutral) face stimuli. Participants with greater attentional bias towards happy faces (but not angry faces) reported higher trait resilience. However, an attentional bias towards angry stimuli moderated this effect: The attentional bias towards happy faces was only predictive for resilience in those individuals who also endorsed an attentional bias towards angry stimuli. An attentional bias towards positive emotional stimuli may thus be a protective factor contributing to stress resilience, specifically in those individuals who also endorse an attentional bias towards negative emotional stimuli. Our findings therefore suggest a novel target for prevention and treatment interventions addressing stress-related psychopathology.

  5. Minimum Bias Trigger in ATLAS

    International Nuclear Information System (INIS)

    Kwee, Regina

    2010-01-01

    Since the restart of the LHC in November 2009, ATLAS has collected inelastic pp collisions to perform first measurements on charged particle densities. These measurements will help to constrain various models describing phenomenologically soft parton interactions. Understanding the trigger efficiencies for different event types are therefore crucial to minimize any possible bias in the event selection. ATLAS uses two main minimum bias triggers, featuring complementary detector components and trigger levels. While a hardware based first trigger level situated in the forward regions with 2.2 < |η| < 3.8 has been proven to select pp-collisions very efficiently, the Inner Detector based minimum bias trigger uses a random seed on filled bunches and central tracking detectors for the event selection. Both triggers were essential for the analysis of kinematic spectra of charged particles. Their performance and trigger efficiency measurements as well as studies on possible bias sources will be presented. We also highlight the advantage of these triggers for particle correlation analyses. (author)

  6. Bias correction of daily precipitation projected by the CORDEX-Africa ensemble for a sparsely gauged region in West Africa with regionalized distribution parameters

    Science.gov (United States)

    Lorenz, Manuel; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald

    2017-04-01

    Reliable estimates of future climatic conditions are indispensable for the sustainable planning of agricultural activities in West Africa. Precipitation time series of regional climate models (RCMs) typically exhibit a bias in the distribution of both rainfall intensities and wet day frequencies. Furthermore, the annual and monthly sums of precipitation may remarkably vary from the observations in this region. As West Africa experiences a distinct rainy season, sowing dates are oftentimes planned based on the beginning of this rainfall period. A biased representation of the annual cycle of precipitation in the uncorrected RCMs can therefore lead to crop failure. The precipitation ensemble, obtained from the Coordinated Downscaling Experiment CORDEX-Africa, was bias-corrected for the study region in West Africa (extending approximately 343,358 km2) which covers large parts of Burkina Faso, Ghana and Benin. In oder to debias the RCM precipitation simulations, a Quantile-Mapping method was applied to the historical period 1950-2005. For the RCM future projections (2006-2100), the Double-Quantile-Mapping procedure was chosen. This method makes use of the shift in the distribution function of the future precipitation values which allows to incorporate the climate change signal of the RCM projections into the bias correction. As large areas of the study region are ungauged, the assignment of the information from the nearest station to the ungauged location would lead to sharp changes in the estimated statistics from one location to another. Thus, the distribution parameters needed for the Quantile-Mapping were estimated by Kriging the distribution parameters of the available measurement stations. This way it is possible to obtain reasonable estimates of the expected distribution of precipitation at ungauged locations. The presentation will illustrate some aspects and trade-offs in the distribution parameter interpolation as well as an analysis of the uncertainties of the

  7. Is there bias in editorial choice? Yes

    OpenAIRE

    Moustafa, Khaled

    2018-01-01

    Nature has recently published a Correspondence claiming the absence of fame biases in the editorial choice. The topic is interesting and deserves a deeper analysis than it was presented because the reported brief analysis and its conclusion are somewhat biased for many reasons, some of them are discussed here. Since the editorial assessment is a form of peer-review, the biases reported on external peer-reviews would, thus, apply to the editorial assessment, too. The biases would be proportion...

  8. Bias correction and Bayesian analysis of aggregate counts in SAGE libraries

    Directory of Open Access Journals (Sweden)

    Briggs William M

    2010-02-01

    Full Text Available Abstract Background Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context. Conclusions Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression.

  9. Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation

    OpenAIRE

    Rajiv D. Banker

    1993-01-01

    This paper provides a formal statistical basis for the efficiency evaluation techniques of data envelopment analysis (DEA). DEA estimators of the best practice monotone increasing and concave production function are shown to be also maximum likelihood estimators if the deviation of actual output from the efficient output is regarded as a stochastic variable with a monotone decreasing probability density function. While the best practice frontier estimator is biased below the theoretical front...

  10. Australia's Bond Home Bias

    OpenAIRE

    Anil V. Mishra; Umaru B. Conteh

    2014-01-01

    This paper constructs the float adjusted measure of home bias and explores the determinants of bond home bias by employing the International Monetary Fund's high quality dataset (2001 to 2009) on cross-border bond investment. The paper finds that Australian investors' prefer investing in countries with higher economic development and more developed bond markets. Exchange rate volatility appears to be an impediment for cross-border bond investment. Investors prefer investing in countries with ...

  11. Systematic biases in human heading estimation.

    Directory of Open Access Journals (Sweden)

    Luigi F Cuturi

    Full Text Available Heading estimation is vital to everyday navigation and locomotion. Despite extensive behavioral and physiological research on both visual and vestibular heading estimation over more than two decades, the accuracy of heading estimation has not yet been systematically evaluated. Therefore human visual and vestibular heading estimation was assessed in the horizontal plane using a motion platform and stereo visual display. Heading angle was overestimated during forward movements and underestimated during backward movements in response to both visual and vestibular stimuli, indicating an overall multimodal bias toward lateral directions. Lateral biases are consistent with the overrepresentation of lateral preferred directions observed in neural populations that carry visual and vestibular heading information, including MSTd and otolith afferent populations. Due to this overrepresentation, population vector decoding yields patterns of bias remarkably similar to those observed behaviorally. Lateral biases are inconsistent with standard bayesian accounts which predict that estimates should be biased toward the most common straight forward heading direction. Nevertheless, lateral biases may be functionally relevant. They effectively constitute a perceptual scale expansion around straight ahead which could allow for more precise estimation and provide a high gain feedback signal to facilitate maintenance of straight-forward heading during everyday navigation and locomotion.

  12. The Accuracy Enhancing Effect of Biasing Cues

    NARCIS (Netherlands)

    W. Vanhouche (Wouter); S.M.J. van Osselaer (Stijn)

    2009-01-01

    textabstractExtrinsic cues such as price and irrelevant attributes have been shown to bias consumers’ product judgments. Results in this article replicate those findings in pretrial judgments but show that such biasing cues can improve quality judgments at a later point in time. Initially biasing

  13. How we categorize objects is related to how we remember them: The shape bias as a memory bias

    Science.gov (United States)

    Vlach, Haley A.

    2016-01-01

    The “shape bias” describes the phenomenon that, after a certain point in development, children and adults generalize object categories based upon shape to a greater degree than other perceptual features. The focus of research on the shape bias has been to examine the types of information that learners attend to in one moment in time. The current work takes a different approach by examining whether learners' categorical biases are related to their retention of information across time. In three experiments, children's (N = 72) and adults' (N = 240) memory performance for features of objects was examined in relation to their categorical biases. The results of these experiments demonstrated that the number of shape matches chosen during the shape bias task significantly predicted shape memory. Moreover, children and adults with a shape bias were more likely to remember the shape of objects than they were the color and size of objects. Taken together, this work suggests the development of a shape bias may engender better memory for shape information. PMID:27454236

  14. Positioning of Weight Bias: Moving towards Social Justice.

    Science.gov (United States)

    Nutter, Sarah; Russell-Mayhew, Shelly; Alberga, Angela S; Arthur, Nancy; Kassan, Anusha; Lund, Darren E; Sesma-Vazquez, Monica; Williams, Emily

    2016-01-01

    Weight bias is a form of stigma with detrimental effects on the health and wellness of individuals with large bodies. Researchers from various disciplines have recognized weight bias as an important topic for public health and for professional practice. To date, researchers from various areas have approached weight bias from independent perspectives and from differing theoretical orientations. In this paper, we examined the similarities and differences between three perspectives (i.e., weight-centric, non-weight-centric (health-centric), and health at every size) used to understand weight bias and approach weight bias research with regard to (a) language about people with large bodies, (b) theoretical position, (c) identified consequences of weight bias, and (d) identified influences on weight-based social inequity. We suggest that, despite differences, each perspective acknowledges the negative influences that position weight as being within individual control and the negative consequences of weight bias. We call for recognition and discussion of weight bias as a social justice issue in order to change the discourse and professional practices extended towards individuals with large bodies. We advocate for an emphasis on social justice as a uniting framework for interdisciplinary research on weight bias.

  15. An inclusive taxonomy of behavioral biases

    OpenAIRE

    David Peón; Manel Antelo; Anxo Calvo-Silvosa

    2017-01-01

    This paper overviews the theoretical and empirical research on behavioral biases and their influence in the literature. To provide a systematic exposition, we present a unified framework that takes the reader through an original taxonomy, based on the reviews of relevant authors in the field. In particular, we establish three broad categories that may be distinguished: heuristics and biases; choices, values and frames; and social factors. We then describe the main biases within each category,...

  16. Automaticity of cognitive biases in addictive behaviours: further evidence with gamblers.

    Science.gov (United States)

    McCusker, C G; Gettings, B

    1997-11-01

    The hypotheses that automatic, non-volitional, attentional and memory biases for addiction-related constructs exist is tested with compulsive gamblers. An independent groups design was employed. Processing of gambling, compared to neutral and drug-related information was examined in 15 gamblers recruited from new members of Gamblers Anonymous. Comparisons were made with the performance of their spouses (N = 15) to help distinguish addiction mechanisms from more non-specific emotional experiences with gambling, and an independent control group (N = 15), recruited from the staff and students of a university department. A modified Stroop procedure was first employed. Automative cognitive interference was assessed relatively, by comparing colour-naming times on the gambling, drug and neutral Stroops. A subsequent word-stem completion task of implicit memory was then used to assess selective and automatic priming of the gambling constructs in memory. Only the gamblers showed selective and automatic interference for gambling-related constructs on the Stroop task. Spouses behaved like the control group on this task. An implicit memory bias for gambling-related words was statistically detected only in the gamblers compared to the control group, although the trend was similar in the comparison with spouses. Further evidence for the specificity of these effects was obtained in subgroup comparisons involving fruit-machine with racing gamblers. Results are generally consistent with an automaticity in the cognitive biases gamblers show for gambling-related information. Implications for cognitive understanding and treatments are highlighted.

  17. Significance analysis of lexical bias in microarray data

    Directory of Open Access Journals (Sweden)

    Falkow Stanley

    2003-04-01

    Full Text Available Abstract Background Genes that are determined to be significantly differentially regulated in microarray analyses often appear to have functional commonalities, such as being components of the same biochemical pathway. This results in certain words being under- or overrepresented in the list of genes. Distinguishing between biologically meaningful trends and artifacts of annotation and analysis procedures is of the utmost importance, as only true biological trends are of interest for further experimentation. A number of sophisticated methods for identification of significant lexical trends are currently available, but these methods are generally too cumbersome for practical use by most microarray users. Results We have developed a tool, LACK, for calculating the statistical significance of apparent lexical bias in microarray datasets. The frequency of a user-specified list of search terms in a list of genes which are differentially regulated is assessed for statistical significance by comparison to randomly generated datasets. The simplicity of the input files and user interface targets the average microarray user who wishes to have a statistical measure of apparent lexical trends in analyzed datasets without the need for bioinformatics skills. The software is available as Perl source or a Windows executable. Conclusion We have used LACK in our laboratory to generate biological hypotheses based on our microarray data. We demonstrate the program's utility using an example in which we confirm significant upregulation of SPI-2 pathogenicity island of Salmonella enterica serovar Typhimurium by the cation chelator dipyridyl.

  18. Cognitive biases can affect moral intuitions about cognitive enhancement

    Directory of Open Access Journals (Sweden)

    Lucius eCaviola

    2014-10-01

    Full Text Available Research into cognitive biases that impair human judgment has mostly been applied to the area of economic decision-making. Ethical decision-making has been comparatively neglected. Since ethical decisions often involve very high individual as well as collective stakes, analyzing how cognitive biases affect them can be expected to yield important results. In this theoretical article, we consider the ethical debate about cognitive enhancement (CE and suggest a number of cognitive biases that are likely to affect moral intuitions and judgments about CE: status quo bias, loss aversion, risk aversion, omission bias, scope insensitivity, nature bias, and optimistic bias. We find that there are more well-documented biases that are likely to cause irrational aversion to CE than biases in the opposite direction. This suggests that common attitudes about CE are predominantly negatively biased. Within this new perspective, we hope that subsequent research will be able to elaborate this hypothesis and develop effective de-biasing techniques that can help increase the rationality of the public CE debate and thus improve our ethical decision-making.

  19. QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials.

    Science.gov (United States)

    Malyarenko, Dariya I; Wilmes, Lisa J; Arlinghaus, Lori R; Jacobs, Michael A; Huang, Wei; Helmer, Karl G; Taouli, Bachir; Yankeelov, Thomas E; Newitt, David; Chenevert, Thomas L

    2016-12-01

    Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, -35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.

  20. Determination and Correction of Persistent Biases in Quantum Annealers

    Science.gov (United States)

    2016-08-25

    for all of the qubits. Narrowing of the bias distribution. To show the correctability of the persistent biases , we ran the experiment described above...this is a promising application for bias correction . Importantly, while the J biases determined here are in general smaller than the h biases , numerical...1Scientific RepoRts | 6:18628 | DOI: 10.1038/srep18628 www.nature.com/scientificreports Determination and correction of persistent biases in quantum

  1. Does neurocognitive function affect cognitive bias toward an emotional stimulus? Association between general attentional ability and attentional bias toward threat

    OpenAIRE

    Hakamata, Yuko; Matsui, Mie; Tagaya, Hirokuni

    2014-01-01

    Background: Although poorer cognitive performance has been found to be associated with anxiety, it remains unclear whether neurocognitive function affects biased cognitive processing toward emotional information. We investigated whether general cognitive function evaluated with a standard neuropsychological test predicts biased cognition, focusing on attentional bias toward threat. Methods: One hundred and five healthy young adults completed a dot-probe task measuring attentional bias and ...

  2. Production bias and cluster annihilation: Why necessary?

    DEFF Research Database (Denmark)

    Singh, B.N.; Trinkaus, H.; Woo, C.H.

    1994-01-01

    the primary cluster density is high. Therefore, a sustained high swelling rate driven by production bias must involve the annihilation of primary clusters at sinks. A number of experimental observations which are unexplainable in terms of the conventional dislocation bias for monointerstitials is considered......-field approach. The production bias approach, on the other hand, is based on the physical features of the cascade damage and is therefore considered to be more appropriate for describing the damage accumulation under cascade damage conditions. However, production bias can not produce high a swelling rate when....... It is found that the production bias and cluster annihilation are necessary to explain these observations, with, in many cases, the explicit consideration of the annihilation of the primary interstitial clusters....

  3. Approach-Induced Biases in Human Information Sampling.

    Directory of Open Access Journals (Sweden)

    Laurence T Hunt

    2016-11-01

    Full Text Available Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach", the selection of which information to sample ("sampling the favorite", and the interaction between information sampling and subsequent choices ("rejecting unsampled options". The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

  4. Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model

    Science.gov (United States)

    Li, S.; Rupp, D. E.; Hawkins, L.; Mote, P.; McNeall, D. J.; Sarah, S.; Wallom, D.; Betts, R. A.

    2017-12-01

    This study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office's atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. The bias-reduction process is done through a series of steps: 1) Generation of perturbed physics ensemble (PPE) through the volunteer computing network weather@home; 2) Using machine learning to train "cheap" and fast statistical emulators of climate model, to rule out regions of parameter spaces that lead to model variants that do not satisfy observational constraints, where the observational constraints (e.g., top-of-atmosphere energy flux, magnitude of annual temperature cycle, summer/winter temperature and precipitation) are introduced sequentially; 3) Designing a new PPE by "pre-filtering" using the emulator results. Steps 1) through 3) are repeated until results are considered to be satisfactory (3 times in our case). The process includes a sensitivity analysis to find dominant parameters for various model output metrics, which reduces the number of parameters to be perturbed with each new PPE. Relative to observational uncertainty, we achieve regional improvements without introducing large biases in other parts of the globe. Our results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate model, in combination with PPEs in systematic model improvement.

  5. Gender bias affects forests worldwide

    Science.gov (United States)

    Marlène Elias; Susan S Hummel; Bimbika S Basnett; Carol J.P. Colfer

    2017-01-01

    Gender biases persist in forestry research and practice. These biases result in reduced scientific rigor and inequitable, ineffective, and less efficient policies, programs, and interventions. Drawing from a two-volume collection of current and classic analyses on gender in forests, we outline five persistent and inter-related themes: gendered governance, tree tenure,...

  6. Weight bias internalization across weight categories among school-aged children. Validation of the Weight Bias Internalization Scale for Children.

    Science.gov (United States)

    Zuba, Anna; Warschburger, Petra

    2018-06-01

    Anti-fat bias is widespread and is linked to the internalization of weight bias and psychosocial problems. The purpose of this study was to examine the internalization of weight bias among children across weight categories and to evaluate the psychometric properties of the Weight Bias Internalization Scale for Children (WBIS-C). Data were collected from 1484 primary school children and their parents. WBIS-C demonstrated good internal consistency (α = .86) after exclusion of Item 1. The unitary factor structure was supported using exploratory and confirmatory factor analyses (factorial validity). Girls and overweight children reported higher WBIS-C scores in comparison to boys and non-overweight peers (known-groups validity). Convergent validity was shown by significant correlations with psychosocial problems. Internalization of weight bias explained additional variance in different indicators of psychosocial well-being. The results suggest that the WBIS-C is a psychometrically sound and informative tool to assess weight bias internalization among children. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Contamination effects on fixed-bias Langmuir probes

    Energy Technology Data Exchange (ETDEWEB)

    Steigies, C. T. [Institut fuer Experimentelle und Angewandte Physik, Christian-Albrechts-Universitaet zu Kiel, 24098 Kiel (Germany); Barjatya, A. [Department of Physical Sciences, Embry-Riddle Aeronautical University, Daytona Beach, Florida 32114 (United States)

    2012-11-15

    Langmuir probes are standard instruments for plasma density measurements on many sounding rockets. These probes can be operated in swept-bias as well as in fixed-bias modes. In swept-bias Langmuir probes, contamination effects are frequently visible as a hysteresis between consecutive up and down voltage ramps. This hysteresis, if not corrected, leads to poorly determined plasma densities and temperatures. With a properly chosen sweep function, the contamination parameters can be determined from the measurements and correct plasma parameters can then be determined. In this paper, we study the contamination effects on fixed-bias Langmuir probes, where no hysteresis type effect is seen in the data. Even though the contamination is not evident from the measurements, it does affect the plasma density fluctuation spectrum as measured by the fixed-bias Langmuir probe. We model the contamination as a simple resistor-capacitor circuit between the probe surface and the plasma. We find that measurements of small scale plasma fluctuations (meter to sub-meter scale) along a rocket trajectory are not affected, but the measured amplitude of large scale plasma density variation (tens of meters or larger) is attenuated. From the model calculations, we determine amplitude and cross-over frequency of the contamination effect on fixed-bias probes for different contamination parameters. The model results also show that a fixed bias probe operating in the ion-saturation region is affected less by contamination as compared to a fixed bias probe operating in the electron saturation region.

  8. Pointwise probability reinforcements for robust statistical inference.

    Science.gov (United States)

    Frénay, Benoît; Verleysen, Michel

    2014-02-01

    Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. The relationship between study sponsorship, risks of bias, and research outcomes in atrazine exposure studies conducted in non-human animals: Systematic review and meta-analysis.

    Science.gov (United States)

    Bero, L; Anglemyer, A; Vesterinen, H; Krauth, D

    2016-01-01

    A critical component of systematic review methodology is the assessment of the risks of bias of studies that are included in the review. There is controversy about whether funding source should be included in a risk of bias assessment of animal toxicology studies. To determine whether industry research sponsorship is associated with methodological biases, the results, or conclusions of animal studies examining the effect of exposure to atrazine on reproductive or developmental outcomes. We searched multiple electronic databases and the reference lists of relevant articles to identify original research studies examining the effect of any dose of atrazine exposure at any life stage on reproduction or development in non-human animals. We compared methodological risks of bias, the conclusions of the studies, the statistical significance of the findings, and the magnitude of effect estimates between industry sponsored and non-industry sponsored studies. Fifty-one studies met the inclusion criteria. There were no differences in methodological risks of bias in industry versus non-industry sponsored studies. 39 studies tested environmentally relevant concentrations of atrazine (11 industry sponsored, 24 non-industry sponsored, 4 with no funding disclosures). Non-industry sponsored studies (12/24, 50.0%) were more likely to conclude that atrazine was harmful compared to industry sponsored studies (2/11, 18.1%) (p value=0.07). A higher proportion of non-industry sponsored studies reported statistically significant harmful effects (8/24, 33.3%) compared to industry-sponsored studies (1/11; 9.1%) (p value=0.13). The association of industry sponsorship with decreased effect sizes for harm outcomes was inconclusive. Our findings support the inclusion of research sponsorship as a risk of bias criterion in tools used to assess risks of bias in animal studies for systematic reviews. The reporting of other empirically based risk of bias criteria for animal studies, such as blinded

  10. Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

    DEFF Research Database (Denmark)

    Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

    2013-01-01

    A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

  11. Heuristic Biases in Mathematical Reasoning

    Science.gov (United States)

    Inglis, Matthew; Simpson, Adrian

    2005-01-01

    In this paper we briefly describe the dual process account of reasoning, and explain the role of heuristic biases in human thought. Concentrating on the so-called matching bias effect, we describe a piece of research that indicates a correlation between success at advanced level mathematics and an ability to override innate and misleading…

  12. Source Credibility and the Biasing Effect of Narrative Information on the Perception of Vaccination Risks.

    Science.gov (United States)

    Haase, Niels; Betsch, Cornelia; Renkewitz, Frank

    2015-08-01

    Immunization rates are below the Global Immunization Vision and Strategy established by the World Health Organization. One reason for this are anti-vaccination activists, who use the Internet to disseminate their agenda, frequently by publishing narrative reports about alleged vaccine adverse events. In health communication, the use of narrative information has been shown to be effectively persuasive. Furthermore, persuasion research indicates that the credibility of an information source may serve as a cue to discount or augment the communicated message. Thus, the present study investigated the effect of source credibility on the biasing effect of narrative information regarding the perception of vaccination risks. 265 participants were provided with statistical information (20%) regarding the occurrence of vaccine adverse events after vaccination against a fictitious disease. This was followed by 20 personalized narratives from an online forum on vaccination experiences. The authors varied the relative frequency of narratives reporting vaccine adverse events (35% vs. 85%), narrative source credibility (anti-vaccination website vs. neutral health forum), and the credibility of the statistical information (reliable data vs. unreliable data vs. control) in a between-subjects design. Results showed a stable narrative bias on risk perception that was not affected by credibility cues. However, narratives from an anti-vaccination website generally led to lower perceptions of vaccination risks.

  13. Evidence of the overconfidence bias in the Egyptian stock market in different market states

    Directory of Open Access Journals (Sweden)

    Ayman H. Metwally

    2015-11-01

    Full Text Available Traditional finance theories fail to explain several anomalies observed in security markets. High levels of market turnover are among the most challenging market puzzles that have been documented in many security markets. Several studies assert the correlation between past market return and current market turnover. Behavioral finance theories assume that overconfidence bias is the reason behind this relation. Hence, this paper aims to study the impact of overconfidence – a behavioral bias stemming from the second building block of behavioral finance “cognitive psychology” and affecting traders’ beliefs and thereby their trading behavior in form of excessive trading. DeBondt and Tahler (1995. The study tests the overconfidence bias in the Egyptian Stock market during the period from 2002 till 2012 on the aggregate market level trough examining the relation between market returns and market turnover in different market states, seeking to document or deny whether overconfidence bias encourages investors to trade or not . The whole period is divided into four sub periods; two tranquil upward trending (2005-2005 and (2005-2008 and two volatile and down ward trending (financial crisis 2008-2010 and the (Egyptian Revolution Period 2010-2012 A quantitative research using secondary data and applying time series statistical techniques is designed. The research is following Statman et al. (2006 methodology. Time series analysis, which is based on four statistical techniques; mainly Vector Auto Regression, Optimal Lag Selection, Impulse Response Function and Granger Causality Tests are being used. Market Turnover ratios are used as proxies for overconfidence. The research finds a significant impact of past market return on current turnover in lag1, then turns negative in lag 2, and returns back positive in lag3, then remains positive and significant until lag5. This is in line with the overconfidence and self-attribution theory of Denial et al

  14. Improved Correction of Misclassification Bias With Bootstrap Imputation.

    Science.gov (United States)

    van Walraven, Carl

    2018-07-01

    Diagnostic codes used in administrative database research can create bias due to misclassification. Quantitative bias analysis (QBA) can correct for this bias, requires only code sensitivity and specificity, but may return invalid results. Bootstrap imputation (BI) can also address misclassification bias but traditionally requires multivariate models to accurately estimate disease probability. This study compared misclassification bias correction using QBA and BI. Serum creatinine measures were used to determine severe renal failure status in 100,000 hospitalized patients. Prevalence of severe renal failure in 86 patient strata and its association with 43 covariates was determined and compared with results in which renal failure status was determined using diagnostic codes (sensitivity 71.3%, specificity 96.2%). Differences in results (misclassification bias) were then corrected with QBA or BI (using progressively more complex methods to estimate disease probability). In total, 7.4% of patients had severe renal failure. Imputing disease status with diagnostic codes exaggerated prevalence estimates [median relative change (range), 16.6% (0.8%-74.5%)] and its association with covariates [median (range) exponentiated absolute parameter estimate difference, 1.16 (1.01-2.04)]. QBA produced invalid results 9.3% of the time and increased bias in estimates of both disease prevalence and covariate associations. BI decreased misclassification bias with increasingly accurate disease probability estimates. QBA can produce invalid results and increase misclassification bias. BI avoids invalid results and can importantly decrease misclassification bias when accurate disease probability estimates are used.

  15. Affective Biases in Humans and Animals.

    Science.gov (United States)

    Robinson, E S J; Roiser, J P

    Depression is one of the most common but poorly understood psychiatric conditions. Although drug treatments and psychological therapies are effective in some patients, many do not achieve full remission and some patients receive no apparent benefit. Developing new improved treatments requires a better understanding of the aetiology of symptoms and evaluation of novel therapeutic targets in pre-clinical studies. Recent developments in our understanding of the basic cognitive processes that may contribute to the development of depression and its treatment offer new opportunities for both clinical and pre-clinical research. This chapter discusses the clinical evidence supporting a cognitive neuropsychological model of depression and antidepressant efficacy, and how this information may be usefully translated to pre-clinical investigation. Studies using neuropsychological tests in depressed patients and at risk populations have revealed basic negative emotional biases and disrupted reward and punishment processing, which may also impact on non-affective cognition. These affective biases are sensitive to antidepressant treatments with early onset effects observed, suggesting an important role in recovery. This clinical work into affective biases has also facilitated back-translation to animals and the development of assays to study affective biases in rodents. These animal studies suggest that, similar to humans, rodents in putative negative affective states exhibit negative affective biases on decision-making and memory tasks. Antidepressant treatments also induce positive biases in these rodent tasks, supporting the translational validity of this approach. Although still in the early stages of development and validation, affective biases in depression have the potential to offer new insights into the clinical condition, as well as facilitating the development of more translational approaches for pre-clinical studies.

  16. Biased managers, organizational design, and incentive provision

    OpenAIRE

    Moreira, Humberto Ataíde; Costa, Cristiano Machado; Ferreira, Daniel Bernardo Soares

    2004-01-01

    Rio de Janeiro We model the tradeoff between the balance and the strength of incentives implicit in the choice between hierarchical and matrix organizational structures. We show that managerial biases determine which structure is optimal: hierarchical forms are preferred when biases are low, while matrix structures are preferred when biases are high.

  17. Biases in Visual, Auditory, and Audiovisual Perception of Space

    Science.gov (United States)

    Odegaard, Brian; Wozny, David R.; Shams, Ladan

    2015-01-01

    Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1) if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors), and (2) whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli). Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers) was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s) driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only improves the

  18. Biases in Visual, Auditory, and Audiovisual Perception of Space.

    Directory of Open Access Journals (Sweden)

    Brian Odegaard

    2015-12-01

    Full Text Available Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1 if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors, and (2 whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli. Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only

  19. Understanding and Overcoming Implicit Gender Bias in Plastic Surgery.

    Science.gov (United States)

    Phillips, Nicole A; Tannan, Shruti C; Kalliainen, Loree K

    2016-11-01

    Although explicit sex-based discrimination has largely been deemed unacceptable in professional settings, implicit gender bias persists and results in a significant lack of parity in plastic surgery and beyond. Implicit gender bias is the result of a complex interplay of cultural and societal expectations, learned behaviors, and standardized associations. As such, both male and female surgeons are subject to its influence. A review of the literature was conducted, examining theories of gender bias, current manifestations of gender bias in plastic surgery and other fields, and interventions designed to address gender bias. Multiple studies demonstrate persistent gender bias that impacts female physicians at all levels of training. Several institutions have enacted successful interventions to identify and address gender bias. Explicit gender bias has largely disappeared, yet unconscious or implicit gender bias persists. A wide-scale commitment to addressing implicit gender bias in plastic surgery is necessary and overdue. Recommendations include immediate actions that can be undertaken on an individual basis, and changes that should be implemented at a national and international level by leaders in the field.

  20. Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Bauman, William H., III

    2010-01-01

    The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind

  1. Bias changing molecule–lead couple and inducing low bias negative differential resistance for electrons acceptor predicted by first-principles study

    International Nuclear Information System (INIS)

    Min, Y.; Fang, J.H.; Zhong, C.G.; Dong, Z.C.; Zhao, Z.Y.; Zhou, P.X.; Yao, K.L.

    2015-01-01

    A first-principles study of the transport properties of 3,13-dimercaptononacene–6,21-dione molecule sandwiched between two gold leads is reported. The strong effect of negative differential resistance with large peak-to-valley ratio of 710% is present under low bias. We found that bias can change molecule–lead couple and induce low bias negative differential resistance for electrons acceptor, which may promise the potential applications in molecular devices with low-power dissipation in the future. - Highlights: • Acceptor is constructed to negative differential resistor (NDR). • NDR effect is present under low bias. • Bias change molecule–lead couple and induce NDR effect

  2. Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

    Science.gov (United States)

    Clark, Kevin B

    2012-01-01

    Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present

  3. Social biases determine spatiotemporal sparseness of ciliate mating heuristics

    Science.gov (United States)

    2012-01-01

    Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The

  4. Mechanism for and method of biasing magnetic sensor

    Science.gov (United States)

    Kautz, David R.

    2007-12-04

    A magnetic sensor package having a biasing mechanism involving a coil-generated, resistor-controlled magnetic field for providing a desired biasing effect. In a preferred illustrated embodiment, the package broadly comprises a substrate; a magnetic sensor element; a biasing mechanism, including a coil and a first resistance element; an amplification mechanism; a filter capacitor element; and an encapsulant. The sensor is positioned within the coil. A current applied to the coil produces a biasing magnetic field. The biasing magnetic field is controlled by selecting a resistance value for the first resistance element which achieves the desired biasing effect. The first resistance element preferably includes a plurality of selectable resistors, the selection of one or more of which sets the resistance value.

  5. Attentional Bias in Human Category Learning: The Case of Deep Learning.

    Science.gov (United States)

    Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

    2018-01-01

    Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures

  6. Attentional Bias in Human Category Learning: The Case of Deep Learning

    Directory of Open Access Journals (Sweden)

    Catherine Hanson

    2018-04-01

    Full Text Available Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987 showed that stimuli can have structures with features that are statistically uncorrelated (separable or statistically correlated (integral within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974. In contrast to humans, a single hidden layer backpropagation (BP neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993. This “failure” to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1 by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2 by investigating whether a Deep Learning (DL network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc., would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993. Second, we show that using the same low dimensional stimuli, Deep Learning (DL, unlike BP but similar to humans, learns separable category structures more quickly than integral category

  7. Mood-congruent free recall bias in anxious individuals is not a consequence of response bias.

    Science.gov (United States)

    Russo, Riccardo; Whittuck, Dora; Roberson, Debi; Dutton, Kevin; Georgiou, George; Fox, Elaine

    2006-05-01

    The status of mood-congruent free recall bias in anxious individuals was evaluated following incidental encoding of target words. Individuals with high and low levels of trait anxiety completed a modified Stroop task, which revealed an attentional bias for threat-related stimuli in anxious individuals. This group was significantly slower in naming the colour in which threat-related words were displayed compared to neutral words. In a subsequent free recall test for the words used in the modified Stroop task, anxious individuals recalled more threat-related words compared to low-anxious people. This difference was significant even when controlling for the false recall of items that had not been presented during study. These results support the view put forward by Russo, Fox, Bellinger, and Nguyen-Van-Tam (2001) that mood-congruent free recall bias in anxious individuals can be observed if the target material is encoded at a relatively shallow level. Moreover, contrary to Dowens and Calvo (2003), the current results show that the memory advantage for threat-related information in anxious individuals is not a consequence of response bias.

  8. White and Black American Children’s Implicit Intergroup Bias

    Science.gov (United States)

    Newheiser, Anna-Kaisa; Olson, Kristina R.

    2011-01-01

    Despite a decline in explicit prejudice, adults and children from majority groups (e.g., White Americans) often express bias implicitly, as assessed by the Implicit Association Test. In contrast, minority-group (e.g., Black American) adults on average show no bias on the IAT. In the present research, representing the first empirical investigation of whether Black children’s IAT responses parallel those of Black adults, we examined implicit bias in 7–11-year-old White and Black American children. Replicating previous findings with adults, whereas White children showed a robust ingroup bias, Black children showed no bias. Additionally, we investigated the role of valuing status in the development of implicit bias. For Black children, explicit preference for high status predicted implicit outgroup bias: Black children who explicitly expressed high preference for rich (vs. poor) people showed an implicit preference for Whites comparable in magnitude to White children’s ingroup bias. Implications for research on intergroup bias are discussed. PMID:22184478

  9. Removing Malmquist bias from linear regressions

    Science.gov (United States)

    Verter, Frances

    1993-01-01

    Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.

  10. Is it feasible to estimate radiosonde biases from interlaced measurements?

    Science.gov (United States)

    Kremser, Stefanie; Tradowsky, Jordis S.; Rust, Henning W.; Bodeker, Greg E.

    2018-05-01

    Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and as such are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure, and humidity, together with well-characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive at measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the

  11. The relationship between weight stigma and eating behavior is explained by weight bias internalization and psychological distress.

    Science.gov (United States)

    O'Brien, Kerry S; Latner, Janet D; Puhl, Rebecca M; Vartanian, Lenny R; Giles, Claudia; Griva, Konstadina; Carter, Adrian

    2016-07-01

    Weight stigma is associated with a range of negative outcomes, including disordered eating, but the psychological mechanisms underlying these associations are not well understood. The present study tested whether the association between weight stigma experiences and disordered eating behaviors (emotional eating, uncontrolled eating, and loss-of-control eating) are mediated by weight bias internalization and psychological distress. Six-hundred and thirty-four undergraduate university students completed an online survey assessing weight stigma, weight bias internalization, psychological distress, disordered eating, along with demographic characteristics (i.e., age, gender, weight status). Statistical analyses found that weight stigma was significantly associated with all measures of disordered eating, and with weight bias internalization and psychological distress. In regression and mediation analyses accounting for age, gender and weight status, weight bias internalization and psychological distress mediated the relationship between weight stigma and disordered eating behavior. Thus, weight bias internalization and psychological distress appear to be important factors underpinning the relationship between weight stigma and disordered eating behaviors, and could be targets for interventions, such as, psychological acceptance and mindfulness therapy, which have been shown to reduce the impact of weight stigma. The evidence for the health consequences resulting from weight stigma is becoming clear. It is important that health and social policy makers are informed of this literature and encouraged develop anti-weight stigma policies for school, work, and medical settings. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Quality assurance and statistical control

    DEFF Research Database (Denmark)

    Heydorn, K.

    1991-01-01

    In scientific research laboratories it is rarely possible to use quality assurance schemes, developed for large-scale analysis. Instead methods have been developed to control the quality of modest numbers of analytical results by relying on statistical control: Analysis of precision serves...... to detect analytical errors by comparing the a priori precision of the analytical results with the actual variability observed among replicates or duplicates. The method relies on the chi-square distribution to detect excess variability and is quite sensitive even for 5-10 results. Interference control...... serves to detect analytical bias by comparing results obtained by two different analytical methods, each relying on a different detection principle and therefore exhibiting different influence from matrix elements; only 5-10 sets of results are required to establish whether a regression line passes...

  13. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

    Science.gov (United States)

    Fourcade, Yoan; Engler, Jan O; Rödder, Dennis; Secondi, Jean

    2014-01-01

    MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

  14. On the relative independence of thinking biases and cognitive ability.

    Science.gov (United States)

    Stanovich, Keith E; West, Richard F

    2008-04-01

    In 7 different studies, the authors observed that a large number of thinking biases are uncorrelated with cognitive ability. These thinking biases include some of the most classic and well-studied biases in the heuristics and biases literature, including the conjunction effect, framing effects, anchoring effects, outcome bias, base-rate neglect, "less is more" effects, affect biases, omission bias, myside bias, sunk-cost effect, and certainty effects that violate the axioms of expected utility theory. In a further experiment, the authors nonetheless showed that cognitive ability does correlate with the tendency to avoid some rational thinking biases, specifically the tendency to display denominator neglect, probability matching rather than maximizing, belief bias, and matching bias on the 4-card selection task. The authors present a framework for predicting when cognitive ability will and will not correlate with a rational thinking tendency. (c) 2008 APA, all rights reserved.

  15. Heuristics, biases and traps in managerial decision making

    Directory of Open Access Journals (Sweden)

    Peter Gál

    2013-01-01

    Full Text Available The aim of the paper is to demonstrate the impact of heuristics, biases and psychological traps on the decision making. Heuristics are unconscious routines people use to cope with the complexity inherent in most decision situations. They serve as mental shortcuts that help people to simplify and structure the information encountered in the world. These heuristics could be quite useful in some situations, while in others they can lead to severe and systematic errors, based on significant deviations from the fundamental principles of statistics, probability and sound judgment. This paper focuses on illustrating the existence of the anchoring, availability, and representativeness heuristics, originally described by Tversky & Kahneman in the early 1970’s. The anchoring heuristic is a tendency to focus on the initial information, estimate or perception (even random or irrelevant number as a starting point. People tend to give disproportionate weight to the initial information they receive. The availability heuristic explains why highly imaginable or vivid information have a disproportionate effect on people’s decisions. The representativeness heuristic causes that people rely on highly specific scenarios, ignore base rates, draw conclusions based on small samples and neglect scope. Mentioned phenomena are illustrated and supported by evidence based on the statistical analysis of the results of a questionnaire.

  16. Effects of Bias Modification Training in Binge Eating Disorder.

    Science.gov (United States)

    Schmitz, Florian; Svaldi, Jennifer

    2017-09-01

    Food-related attentional biases have been identified as maintaining factors in binge eating disorder (BED) as they can trigger a binge episode. Bias modification training may reduce symptoms, as it has been shown to be successful in other appetitive disorders. The aim of this study was to assess and modify food-related biases in BED. It was tested whether biases could be increased and decreased by means of a modified dot-probe paradigm, how long such bias modification persisted, and whether this affected subjective food craving. Participants were randomly assigned to a bias enhancement (attend to food stimulus) group or to a bias reduction (avoid food stimulus) group. Food-related attentional bias was found to be successfully reduced in the bias-reduction group, and effects persisted briefly. Additionally, subjective craving for food was influenced by the intervention, and possible mechanisms are discussed. Given these promising initial results, future research should investigate boundary conditions of the experimental intervention to understand how it could complement treatment of BED. Copyright © 2017. Published by Elsevier Ltd.

  17. Attentional bias in math anxiety.

    Science.gov (United States)

    Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly

    2015-01-01

    Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms.

  18. A retrieval-based approach to eliminating hindsight bias.

    Science.gov (United States)

    Van Boekel, Martin; Varma, Keisha; Varma, Sashank

    2017-03-01

    Individuals exhibit hindsight bias when they are unable to recall their original responses to novel questions after correct answers are provided to them. Prior studies have eliminated hindsight bias by modifying the conditions under which original judgments or correct answers are encoded. Here, we explored whether hindsight bias can be eliminated by manipulating the conditions that hold at retrieval. Our retrieval-based approach predicts that if the conditions at retrieval enable sufficient discrimination of memory representations of original judgments from memory representations of correct answers, then hindsight bias will be reduced or eliminated. Experiment 1 used the standard memory design to replicate the hindsight bias effect in middle-school students. Experiments 2 and 3 modified the retrieval phase of this design, instructing participants beforehand that they would be recalling both their original judgments and the correct answers. As predicted, this enabled participants to form compound retrieval cues that discriminated original judgment traces from correct answer traces, and eliminated hindsight bias. Experiment 4 found that when participants were not instructed beforehand that they would be making both recalls, they did not form discriminating retrieval cues, and hindsight bias returned. These experiments delineate the retrieval conditions that produce-and fail to produce-hindsight bias.

  19. Large-scale assembly bias of dark matter halos

    Energy Technology Data Exchange (ETDEWEB)

    Lazeyras, Titouan; Musso, Marcello; Schmidt, Fabian, E-mail: titouan@mpa-garching.mpg.de, E-mail: mmusso@sas.upenn.edu, E-mail: fabians@mpa-garching.mpg.de [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching (Germany)

    2017-03-01

    We present precise measurements of the assembly bias of dark matter halos, i.e. the dependence of halo bias on other properties than the mass, using curved 'separate universe' N-body simulations which effectively incorporate an infinite-wavelength matter overdensity into the background density. This method measures the LIMD (local-in-matter-density) bias parameters b {sub n} in the large-scale limit. We focus on the dependence of the first two Eulerian biases b {sup E} {sup {sub 1}} and b {sup E} {sup {sub 2}} on four halo properties: the concentration, spin, mass accretion rate, and ellipticity. We quantitatively compare our results with previous works in which assembly bias was measured on fairly small scales. Despite this difference, our findings are in good agreement with previous results. We also look at the joint dependence of bias on two halo properties in addition to the mass. Finally, using the excursion set peaks model, we attempt to shed new insights on how assembly bias arises in this analytical model.

  20. Effects of Inventory Bias on Landslide Susceptibility Calculations

    Science.gov (United States)

    Stanley, T. A.; Kirschbaum, D. B.

    2017-01-01

    Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

  1. Dual-electrode biasing experiments in KT-5C device

    International Nuclear Information System (INIS)

    Yu Yi; Lu Ronghua; Wang Chen; Pan Geshen; Wen Yizhi; Yu Changxuan; Ma Jinxiu; Wan Shude; Liu Wandong

    2005-01-01

    Based on the single biasing electrode experiments to optimize the confinement of plasma in the device of KT-5C tokamak, dual-biasing electrodes were inserted into the KT5C plasma for the first time to explore the enhancement of the effects of biasing and the mechanisms of the biasing. By means of applying different combinations of biasing voltages to the dual electrodes, the changes in E r , which is the key factor for boosting up the Er x B flow shear, were observed. The time evolution showed the inner electrode played a major role in dual-biasing, for it always drew a larger current than the outer one. The outer electrode made little influence. It turned out that the dual-biasing electrodes were as effective as a single one, in improving plasma confinement, for the mechanism of biasing was essentially an edge effect. (author)

  2. Bias-Correction in Vector Autoregressive Models: A Simulation Study

    Directory of Open Access Journals (Sweden)

    Tom Engsted

    2014-03-01

    Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.

  3. Bias-Free Chemically Diverse Test Sets from Machine Learning.

    Science.gov (United States)

    Swann, Ellen T; Fernandez, Michael; Coote, Michelle L; Barnard, Amanda S

    2017-08-14

    Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

  4. Biased lineups: sequential presentation reduces the problem.

    Science.gov (United States)

    Lindsay, R C; Lea, J A; Nosworthy, G J; Fulford, J A; Hector, J; LeVan, V; Seabrook, C

    1991-12-01

    Biased lineups have been shown to increase significantly false, but not correct, identification rates (Lindsay, Wallbridge, & Drennan, 1987; Lindsay & Wells, 1980; Malpass & Devine, 1981). Lindsay and Wells (1985) found that sequential lineup presentation reduced false identification rates, presumably by reducing reliance on relative judgment processes. Five staged-crime experiments were conducted to examine the effect of lineup biases and sequential presentation on eyewitness recognition accuracy. Sequential lineup presentation significantly reduced false identification rates from fair lineups as well as from lineups biased with regard to foil similarity, instructions, or witness attire, and from lineups biased in all of these ways. The results support recommendations that police present lineups sequentially.

  5. Quantum Statistical Testing of a Quantum Random Number Generator

    Energy Technology Data Exchange (ETDEWEB)

    Humble, Travis S [ORNL

    2014-01-01

    The unobservable elements in a quantum technology, e.g., the quantum state, complicate system verification against promised behavior. Using model-based system engineering, we present methods for verifying the opera- tion of a prototypical quantum random number generator. We begin with the algorithmic design of the QRNG followed by the synthesis of its physical design requirements. We next discuss how quantum statistical testing can be used to verify device behavior as well as detect device bias. We conclude by highlighting how system design and verification methods must influence effort to certify future quantum technologies.

  6. Students' gender bias in teaching evaluations

    Directory of Open Access Journals (Sweden)

    Narissra Punyanunt-Carter

    2015-09-01

    Full Text Available The goal of this study was to investigate if there is gender bias in student evaluations. Researchers administered a modified version of the teacher evaluation forms to 58 students (male=30; female=28 in a basic introductory communications class. Half the class was instructed to fill out the survey about a male professor, and the other half a female professor. Researchers broke down the evaluation results question by question in order to give a detailed account of the findings. Results revealed that there is certainly some gender bias at work when students evaluate their instructors. It was also found that gender bias does not significantly affect the evaluations. The results align with other findings in the available literature, which point to some sort of pattern regarding gender bias in evaluations, but it still seems to be inconsequential.  DOI: 10.18870/hlrc.v5i3.234

  7. Measurement and statistical analysis of single-molecule current-voltage characteristics, transition voltage spectroscopy, and tunneling barrier height.

    Science.gov (United States)

    Guo, Shaoyin; Hihath, Joshua; Díez-Pérez, Ismael; Tao, Nongjian

    2011-11-30

    We report on the measurement and statistical study of thousands of current-voltage characteristics and transition voltage spectra (TVS) of single-molecule junctions with different contact geometries that are rapidly acquired using a new break junction method at room temperature. This capability allows one to obtain current-voltage, conductance voltage, and transition voltage histograms, thus adding a new dimension to the previous conductance histogram analysis at a fixed low-bias voltage for single molecules. This method confirms the low-bias conductance values of alkanedithiols and biphenyldithiol reported in literature. However, at high biases the current shows large nonlinearity and asymmetry, and TVS allows for the determination of a critically important parameter, the tunneling barrier height or energy level alignment between the molecule and the electrodes of single-molecule junctions. The energy level alignment is found to depend on the molecule and also on the contact geometry, revealing the role of contact geometry in both the contact resistance and energy level alignment of a molecular junction. Detailed statistical analysis further reveals that, despite the dependence of the energy level alignment on contact geometry, the variation in single-molecule conductance is primarily due to contact resistance rather than variations in the energy level alignment.

  8. An ANOVA approach for statistical comparisons of brain networks.

    Science.gov (United States)

    Fraiman, Daniel; Fraiman, Ricardo

    2018-03-16

    The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

  9. Simulation of HPIB propagation in biased charge collector

    International Nuclear Information System (INIS)

    Li Hongyu; Qiu Aici

    2004-01-01

    A 2.5D PIC simulation using KARAT code for inner charge propagation within biased charge collector for measuring HPIB is presented. The simulation results indicate that the charges were neutralized but the current non-neutralized in the biased charge collector. The influence of ions collected vs biased voltage of the collector was also simulated. -800 V biased voltage can meet the measurement of 500 keV HPIB, and this is consistent with the experimental results

  10. Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimates.

    Science.gov (United States)

    Thompson, Wesley K; Holland, Dominic

    2011-07-01

    A series of reports have recently appeared using tensor based morphometry statistically-defined regions of interest, Stat-ROIs, to quantify longitudinal atrophy in structural MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This commentary focuses on one of these reports, Hua et al. (2010), but the issues raised here are relevant to the others as well. Specifically, we point out a temporal pattern of atrophy in subjects with Alzheimer's disease and mild cognitive impairment whereby the majority of atrophy in two years occurs within the first 6 months, resulting in overall elevated estimated rates of change. Using publicly-available ADNI data, this temporal pattern is also found in a group of identically-processed healthy controls, strongly suggesting that methodological bias is corrupting the measures. The resulting bias seriously impacts the validity of conclusions reached using these measures; for example, sample size estimates reported by Hua et al. (2010) may be underestimated by a factor of five to sixteen. Copyright © 2011 Elsevier Inc. All rights reserved.

  11. New Trends in Magnetic Exchange Bias

    Science.gov (United States)

    Mougin, Alexandra; Mangin, Stéphane; Bobo, Jean-Francois; Loidl, Alois

    2005-05-01

    The study of layered magnetic structures is one of the hottest topics in magnetism due to the growing attraction of applications in magnetic sensors and magnetic storage media, such as random access memory. For almost half a century, new discoveries have driven researchers to re-investigate magnetism in thin film structures. Phenomena such as giant magnetoresistance, tunneling magnetoresistance, exchange bias and interlayer exchange coupling led to new ideas to construct devices, based not only on semiconductors but on a variety of magnetic materials Upon cooling fine cobalt particles in a magnetic field through the Néel temperature of their outer antiferromagnetic oxide layer, Meiklejohn and Bean discovered exchange bias in 1956. The exchange bias effect through which an antiferromagnetic AF layer can cause an adjacent ferromagnetic F layer to develop a preferred direction of magnetization, is widely used in magnetoelectronics technology to pin the magnetization of a device reference layer in a desired direction. However, the origin and effects due to exchange interaction across the interface between antiferromagneic and ferromagnetic layers are still debated after about fifty years of research, due to the extreme difficulty associated with the determination of the magnetic interfacial structure in F/AF bilayers. Indeed, in an AF/F bilayer system, the AF layer acts as “the invisible man” during conventional magnetic measurements and the presence of the exchange coupling is evidenced indirectly through the unusual behavior of the adjacent F layer. Basically, the coercive field of the F layer increases in contact with the AF and, in some cases, its hysteresis loop is shifted by an amount called exchange bias field. Thus, AF/F exchange coupling generates a new source of anisotropy in the F layer. This induced anisotropy strongly depends on basic features such as the magnetocrystalline anisotropy, crystallographic and spin structures, defects, domain patterns etc

  12. Implementation of linear bias corrections for calorimeters at Mound

    International Nuclear Information System (INIS)

    Barnett, T.M.

    1993-01-01

    In the past, Mound has generally made relative bias corrections as part of the calibration of individual calorimeters. The correction made was the same over the entire operating range of the calorimeter, regardless of the magnitude of the range. Recently, an investigation was performed to check the relevancy of using linear bias corrections to calibrate the calorimeters. The bias is obtained by measuring calibrated plutonium and/or electrical heat standards over the operating range of the calorimeter. The bias correction is then calculated using a simple least squares fit (y = mx + b) of the bias in milliwatts over the operating range of the calorimeter in watts. The equation used is B i = B 0 + (B w * W m ), where B i is the bias at any given power in milliwatts, B 0 is the intercept (absolute bias in milliwatts), B w is the slope (relative bias in milliwatts per watt), and W m is the measured power in watts. The results of the study showed a decrease in the random error of bias corrected data for most of the calorimeters which are operated over a large wattage range (greater than an order of magnitude). The linear technique for bias correction has been fully implemented at Mound and has been included in the Technical Manual, ''A Measurement Control Program for Radiometric Calorimeters at Mound'' (MD-21900)

  13. Revealing the Cosmic Web-dependent Halo Bias

    Science.gov (United States)

    Yang, Xiaohu; Zhang, Youcai; Lu, Tianhuan; Wang, Huiyuan; Shi, Feng; Tweed, Dylan; Li, Shijie; Luo, Wentao; Lu, Yi; Yang, Lei

    2017-10-01

    Halo bias is the one of the key ingredients of the halo models. It was shown at a given redshift to be only dependent, to the first order, on the halo mass. In this study, four types of cosmic web environments—clusters, filaments, sheets, and voids—are defined within a state-of-the-art high-resolution N-body simulation. Within these environments, we use both halo-dark matter cross correlation and halo-halo autocorrelation functions to probe the clustering properties of halos. The nature of the halo bias differs strongly between the four different cosmic web environments described here. With respect to the overall population, halos in clusters have significantly lower biases in the {10}11.0˜ {10}13.5 {h}-1 {M}⊙ mass range. In other environments, however, halos show extremely enhanced biases up to a factor 10 in voids for halos of mass ˜ {10}12.0 {h}-1 {M}⊙ . Such a strong cosmic web environment dependence in the halo bias may play an important role in future cosmological and galaxy formation studies. Within this cosmic web framework, the age dependency of halo bias is found to be only significant in clusters and filaments for relatively small halos ≲ {10}12.5 {h}-1 {M}⊙ .

  14. Dwalingen in de methodologie. II. Bias door vragenlijsten

    DEFF Research Database (Denmark)

    Pouwer, F; Van Der Ploeg, Henk M; Bramsen, I

    1998-01-01

    Some characteristics of self-report questionnaires can result in bias in responding. When a test item or a questionnaire is biased, the observed scores form an imprecise measurement of reality as a consequence of systematic errors of measurement. Causes of such bias are: unclear instructions, vague...

  15. Non-Gaussian bias: insights from discrete density peaks

    CERN Document Server

    Desjacques, Vincent; Riotto, Antonio

    2013-01-01

    Corrections induced by primordial non-Gaussianity to the linear halo bias can be computed from a peak-background split or the widespread local bias model. However, numerical simulations clearly support the prediction of the former, in which the non-Gaussian amplitude is proportional to the linear halo bias. To understand better the reasons behind the failure of standard Lagrangian local bias, in which the halo overdensity is a function of the local mass overdensity only, we explore the effect of a primordial bispectrum on the 2-point correlation of discrete density peaks. We show that the effective local bias expansion to peak clustering vastly simplifies the calculation. We generalize this approach to excursion set peaks and demonstrate that the resulting non-Gaussian amplitude, which is a weighted sum of quadratic bias factors, precisely agrees with the peak-background split expectation, which is a logarithmic derivative of the halo mass function with respect to the normalisation amplitude. We point out tha...

  16. Recognizing and reducing cognitive bias in clinical and forensic neurology.

    Science.gov (United States)

    Satya-Murti, Saty; Lockhart, Joseph

    2015-10-01

    In medicine, cognitive errors form the basis of bias in clinical practice. Several types of bias are common and pervasive, and may lead to inaccurate diagnosis or treatment. Forensic and clinical neurology, even when aided by current technologies, are still dependent on cognitive interpretations, and therefore prone to bias. This article discusses 4 common biases that can lead the clinician astray. They are confirmation bias (selective gathering of and neglect of contradictory evidence); base rate bias (ignoring or misusing prevailing base rate data); hindsight bias (oversimplification of past causation); and good old days bias (the tendency for patients to misremember and exaggerate their preinjury functioning). We briefly describe strategies adopted from the field of psychology that could minimize bias. While debiasing is not easy, reducing such errors requires awareness and acknowledgment of our susceptibility to these cognitive distortions.

  17. Attentional Bias in Math Anxiety

    Directory of Open Access Journals (Sweden)

    Orly eRubinsten

    2015-10-01

    Full Text Available Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety as well (i.e., a persistent negative reaction to math. Twenty seven participants (14 with high levels of math anxiety and 13 with low levels of math anxiety were presented with a novel computerized numerical version of the well established dot probe task. One of 6 types of prime stimuli, either math related or typically neutral, were presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks. Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in math anxiety. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words. These findings suggest that attentional bias is linked to unduly intense math anxiety symptoms.

  18. Exchange bias in nearly perpendicularly coupled ferromagnetic/ferromagnetic system

    International Nuclear Information System (INIS)

    Bu, K.M.; Kwon, H.Y.; Oh, S.W.; Won, C.

    2012-01-01

    Exchange bias phenomena appear not only in ferromagnetic/antiferromagnetic systems but also in ferromagnetic/ferromagnetic systems in which two layers are nearly perpendicularly coupled. We investigated the origin of the symmetry-breaking mechanism and the relationship between the exchange bias and the system's energy parameters. We compared the results of computational Monte Carlo simulations with those of theoretical model calculation. We found that the exchange bias exhibited nonlinear behaviors, including sign reversal and singularities. These complicated behaviors were caused by two distinct magnetization processes depending on the interlayer coupling strength. The exchange bias reached a maximum at the transition between the two magnetization processes. - Highlights: ► Exchange bias phenomena are found in perpendicularly coupled F/F systems. ► Exchange bias exhibits nonlinear behaviors, including sign reversal and singularities. ► These complicated behaviors were caused by two distinct magnetization processes. ► Exchange bias reached a maximum at the transition between the two magnetization processes. ► We established an equation to maximize the exchange bias in perpendicularly coupled F/F system.

  19. Blind signal processing algorithms under DC biased Gaussian noise

    Science.gov (United States)

    Kim, Namyong; Byun, Hyung-Gi; Lim, Jeong-Ok

    2013-05-01

    Distortions caused by the DC-biased laser input can be modeled as DC biased Gaussian noise and removing DC bias is important in the demodulation process of the electrical signal in most optical communications. In this paper, a new performance criterion and a related algorithm for unsupervised equalization are proposed for communication systems in the environment of channel distortions and DC biased Gaussian noise. The proposed criterion utilizes the Euclidean distance between the Dirac-delta function located at zero on the error axis and a probability density function of biased constant modulus errors, where constant modulus error is defined by the difference between the system out and a constant modulus calculated from the transmitted symbol points. From the results obtained from the simulation under channel models with fading and DC bias noise abruptly added to background Gaussian noise, the proposed algorithm converges rapidly even after the interruption of DC bias proving that the proposed criterion can be effectively applied to optical communication systems corrupted by channel distortions and DC bias noise.

  20. Bias-correction in vector autoregressive models: A simulation study

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...... and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also...... of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias....

  1. Visualizing Statistical Mix Effects and Simpson's Paradox.

    Science.gov (United States)

    Armstrong, Zan; Wattenberg, Martin

    2014-12-01

    We discuss how "mix effects" can surprise users of visualizations and potentially lead them to incorrect conclusions. This statistical issue (also known as "omitted variable bias" or, in extreme cases, as "Simpson's paradox") is widespread and can affect any visualization in which the quantity of interest is an aggregated value such as a weighted sum or average. Our first contribution is to document how mix effects can be a serious issue for visualizations, and we analyze how mix effects can cause problems in a variety of popular visualization techniques, from bar charts to treemaps. Our second contribution is a new technique, the "comet chart," that is meant to ameliorate some of these issues.

  2. The threshold bias model: a mathematical model for the nomothetic approach of suicide.

    Science.gov (United States)

    Folly, Walter Sydney Dutra

    2011-01-01

    Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health.

  3. Identifying User Profiles from Statistical Grouping Methods

    Directory of Open Access Journals (Sweden)

    Francisco Kelsen de Oliveira

    2018-02-01

    Full Text Available This research aimed to group users into subgroups according to their levels of knowledge about technology. Statistical hierarchical and non-hierarchical clustering methods were studied, compared and used in the creations of the subgroups from the similarities of the skill levels with these users’ technology. The research sample consisted of teachers who answered online questionnaires about their skills with the use of software and hardware with educational bias. The statistical methods of grouping were performed and showed the possibilities of groupings of the users. The analyses of these groups allowed to identify the common characteristics among the individuals of each subgroup. Therefore, it was possible to define two subgroups of users, one with skill in technology and another with skill with technology, so that the partial results of the research showed two main algorithms for grouping with 92% similarity in the formation of groups of users with skill with technology and the other with little skill, confirming the accuracy of the techniques of discrimination against individuals.

  4. Recent Advances in Attention Bias Modification for Substance Addictions

    Directory of Open Access Journals (Sweden)

    Melvyn Weibin Zhang

    2018-04-01

    Full Text Available Research on attentional bias modification has increased since 2014. A recent meta-analysis demonstrates evidence for bias modification for substance disorders, including alcohol and tobacco use disorders. Several pharmacological trials have shown that pharmacological agents can attenuate and modify such attentional bias. The pharmacological trials that have appeared to date have produced mixed results, which has clinical implications. Developments in Internet and mobile technologies have transformed how attention bias modification is currently being achieved. There remains great potential for further research that examines the efficacy of technology-aided attention bias interventions.

  5. Giant exchange bias in MnPd/Co bilayers

    International Nuclear Information System (INIS)

    Nguyen Thanh Nam; Nguyen Phu Thuy; Nguyen Anh Tuan; Nguyen Nguyen Phuoc; Suzuki, Takao

    2007-01-01

    A systematic study of exchange bias in MnPd/Co bilayers has been carried out, where the dependences of exchange bias, unidirectional anisotropy constant and coercivity on the thicknesses of MnPd and Co layers were investigated. A huge unidirectional anisotropy constant, J K =2.5erg/cm 2 was observed, which is in reasonable agreement with the theoretical prediction based on the model by Meiklejohn and Bean. The angular dependences of exchange bias field and coercivity have also been examined showing that both exchange bias and coercivity follow 1/cosα rule

  6. Health risk perception, optimistic bias, and personal satisfaction.

    Science.gov (United States)

    Bränström, Richard; Brandberg, Yvonne

    2010-01-01

    To examine change in risk perception and optimistic bias concerning behavior-linked health threats and environmental health threats between adolescence and young adulthood and how these factors related to personal satisfaction. In 1996 and 2002, 1624 adolescents responded to a mailed questionnaire. Adolescents showed strong positive optimistic bias concerning behaviorlinked risks, and this optimistic bias increased with age. Increase in optimistic bias over time predicted increase in personal satisfaction. The capacity to process and perceive potential threats in a positive manner might be a valuable human ability positively influencing personal satisfaction and well-being.

  7. Some design considerations for perpendicular biased ferrite tuners

    International Nuclear Information System (INIS)

    Enchevich, I.B.; Poirier, R.L.

    1994-10-01

    Recently remarkable progress has been achieved in the development of perpendicular biased ferrite tuned rf resonators for fast cycled synchrotrons. Compared with the broadly used parallel biased rf cavities they provide higher resonator quality factor Q. However when designing perpendicular biased cavities, special attention should be paid to the methods to provide eddy current suppression in the resonator walls, the ferrite nonlinearity influence, the generated heat removal, the fast self resonant frequency control. The prospective of a faster additional biasing system are discussed and conclusions are drawn. (author). 8 refs., 6 figs

  8. Supernovae - A new selection effect. [statistical distribution in and radial distance from center of parent galaxy

    Science.gov (United States)

    Shaw, R. L.

    1979-01-01

    A sample of 228 supernovae that occurred in galaxies with known redshifts is used to show that the mean projected linear supernova distance from the center of the parent galaxy increases with increasing redshift. This effect is interpreted as an observational bias: the discovery rate of supernovae is reduced in the inner parts of distant, poorly resolved galaxies. Even under the optimistic assumption that no selection effects work in galaxies closer than 33 Mpc, about 50% of all supernovae are lost in the inner regions of galaxies beyond 150 Mpc. This observational bias must be taken into account in the derivation of statistical properties of supernovae.

  9. Grandfather regulations, new source bias, and state air toxics regulations

    International Nuclear Information System (INIS)

    Levinson, Arik

    1999-01-01

    This paper uses plant-level data from the Census of Manufactures and the variation in toxic air pollution regulations across states to measure the effects of laws that are more stringent for new sources of pollution than for existing sources (so-called 'grandfather' regulations). Of particular interest is the resulting 'new source bias' and its effects on capital vintage and investment. Two industries are examined: commercial printing, which has a local product market; and paint manufacturing, which has a more national market. In general, there seem to be no statistically significant differences in capital vintage or investment between plants in states that grandfather new sources of pollution, plants in states that have no air toxics regulations, and plants in states that regulate both new and existing sources

  10. Development of Heuristic Bias Detection in Elementary School

    Science.gov (United States)

    De Neys, Wim; Feremans, Vicky

    2013-01-01

    Although human reasoning is often biased by intuitive heuristics, recent studies have shown that adults and adolescents detect the biased nature of their judgments. The present study focused on the development of this critical bias sensitivity by examining the detection skills of young children in elementary school. Third and 6th graders were…

  11. Statistical Multiplicity in Systematic Reviews of Anaesthesia Interventions: A Quantification and Comparison between Cochrane and Non-Cochrane Reviews

    DEFF Research Database (Denmark)

    Imberger, Georgina; Vejlby, Alexandra Hedvig Damgaard; Hansen, Sara Bohnstedt

    2011-01-01

    Systematic reviews with meta-analyses often contain many statistical tests. This multiplicity may increase the risk of type I error. Few attempts have been made to address the problem of statistical multiplicity in systematic reviews. Before the implications are properly considered, the size...... of systematic reviews and aimed to assess whether this quantity is different in Cochrane and non-Cochrane reviews....... of the issue deserves clarification. Because of the emphasis on bias evaluation and because of the editorial processes involved, Cochrane reviews may contain more multiplicity than their non-Cochrane counterparts. This study measured the quantity of statistical multiplicity present in a population...

  12. A Statistical Framework for the Functional Analysis of Metagenomes

    Energy Technology Data Exchange (ETDEWEB)

    Sharon, Itai; Pati, Amrita; Markowitz, Victor; Pinter, Ron Y.

    2008-10-01

    Metagenomic studies consider the genetic makeup of microbial communities as a whole, rather than their individual member organisms. The functional and metabolic potential of microbial communities can be analyzed by comparing the relative abundance of gene families in their collective genomic sequences (metagenome) under different conditions. Such comparisons require accurate estimation of gene family frequencies. They present a statistical framework for assessing these frequencies based on the Lander-Waterman theory developed originally for Whole Genome Shotgun (WGS) sequencing projects. They also provide a novel method for assessing the reliability of the estimations which can be used for removing seemingly unreliable measurements. They tested their method on a wide range of datasets, including simulated genomes and real WGS data from sequencing projects of whole genomes. Results suggest that their framework corrects inherent biases in accepted methods and provides a good approximation to the true statistics of gene families in WGS projects.

  13. 3 keys to defeating unconscious bias watch, think, act

    CERN Document Server

    Thiederman, Sondra

    2015-01-01

    Have you ever had a biased thought? If the answer is “yes,” join the club. Everybody has biases and, although that doesn’t make us bad people, it does mean we compromise our ability to get along with people who are different from us. The good news is, there’s a lot we can do to defeat bias. Calling on Dr. Sondra Thiederman’s twenty-five years of experience in the diversity/inclusion field, the book lays out an innovative WATCH, THINK, ACT strategy that each of us can immediately apply to the task. Easy-to-read and filled with anecdotes and activities, 3 Keys shows the reader: • How to WATCH their thoughts, experiences, and actions to identify unconscious biases and target them for extinction. • How to THINK in such a way as to weaken and control our biases. • How to ACT to defeat our biases and cultivate the kind of common ground that we know to be inhospitable to the survival of bias. Designed to motivate real change, the answer to defeating our biases is in these pages. The rest is up to you...

  14. How does the cosmic web impact assembly bias?

    Science.gov (United States)

    Musso, M.; Cadiou, C.; Pichon, C.; Codis, S.; Kraljic, K.; Dubois, Y.

    2018-06-01

    The mass, accretion rate, and formation time of dark matter haloes near protofilaments (identified as saddle points of the potential) are analytically predicted using a conditional version of the excursion set approach in its so-called upcrossing approximation. The model predicts that at fixed mass, mass accretion rate and formation time vary with orientation and distance from the saddle, demonstrating that assembly bias is indeed influenced by the tides imposed by the cosmic web. Starved, early-forming haloes of smaller mass lie preferentially along the main axis of filaments, while more massive and younger haloes are found closer to the nodes. Distinct gradients for distinct tracers such as typical mass and accretion rate occur because the saddle condition is anisotropic, and because the statistics of these observables depend on both the conditional means and their covariances. The theory is extended to other critical points of the potential field. The response of the mass function to variations of the matter density field (the so-called large-scale bias) is computed, and its trend with accretion rate is shown to invert along the filament. The signature of this model should correspond at low redshift to an excess of reddened galactic hosts at fixed mass along preferred directions, as recently reported in spectroscopic and photometric surveys and in hydrodynamical simulations. The anisotropy of the cosmic web emerges therefore as a significant ingredient to describe jointly the dynamics and physics of galaxies, e.g. in the context of intrinsic alignments or morphological diversity.

  15. Analysis of microstructural evolution driven by production bias

    International Nuclear Information System (INIS)

    Woo, C.H.; Semenov, A.A.; Singh, B.N.

    1993-01-01

    The concept of production bias was first considered in the preceding workshop in this series at Silkeborg in 1989. Since then, much work has been done to investigate the validity of the concept, and its usefulness in complementing the current theory of microstructure evolution based solely on the sink bias (e.g., dislocation bias) as a driving force. Comparison of the theory with experimental results clearly supports the concept. The present paper reviews and summarizes these investigations, and arrives at the following conclusions: a) the concept of production bias is consistent with the results of other works which indicates that, under cascade damage conditions, the effective rate of point-defect production is only a small fraction of the NRT displacement production rate; b) the defect accumulation under cascade damage conditions can be understood in terms of production bias; and c) although the existence of conventional dislocation bias due to point-defect dislocation interaction is not questioned, it does not seem to play any major role in the accumulation of defects under cascade damage conditions at elevated temperatures. (orig.)

  16. Forecaster Behaviour and Bias in Macroeconomic Forecasts

    OpenAIRE

    Roy Batchelor

    2007-01-01

    This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

  17. Interpreting trial results following use of different intention-to-treat approaches for preventing attrition bias: a meta-epidemiological study protocol.

    Science.gov (United States)

    Dossing, Anna; Tarp, Simon; Furst, Daniel E; Gluud, Christian; Beyene, Joseph; Hansen, Bjarke B; Bliddal, Henning; Christensen, Robin

    2014-09-26

    When participants drop out of randomised clinical trials, as frequently happens, the intention-to-treat (ITT) principle does not apply, potentially leading to attrition bias. Data lost from patient dropout/lack of follow-up are statistically addressed by imputing, a procedure prone to bias. Deviations from the original definition of ITT are referred to as modified intention-to-treat (mITT). As yet, the impact of the potential bias associated with mITT has not been assessed. Our objective is to investigate potential bias and disadvantages of performing mITT and evaluate possible concerns when executing different mITT approaches in meta-analyses. Using meta-epidemiology on randomised trials considered less prone to bias (ie, good internal validity) and assessing biological or targeted agents in patients with rheumatoid arthritis, we will meta-analyse data from 10 biological and targeted drugs based on collections of trials that would correspond to 10 individual meta-analyses. This study will enhance transparency for evaluating mITT treatment effects described in meta-analyses. The intended audience will include healthcare researchers, policymakers and clinicians. Results of the study will be disseminated by peer-review publication. In PROSPERO CRD42013006702, 11. December 2013. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  18. Modeling Temporal Bias of Uplift Events in Recommender Systems

    KAUST Repository

    Altaf, Basmah

    2013-05-08

    Today, commercial industry spends huge amount of resources in advertisement campaigns, new marketing strategies, and promotional deals to introduce their product to public and attract a large number of customers. These massive investments by a company are worthwhile because marketing tactics greatly influence the consumer behavior. Alternatively, these advertising campaigns have a discernible impact on recommendation systems which tend to promote popular items by ranking them at the top, resulting in biased and unfair decision making and loss of customers’ trust. The biasing impact of popularity of items on recommendations, however, is not fixed, and varies with time. Therefore, it is important to build a bias-aware recommendation system that can rank or predict items based on their true merit at given time frame. This thesis proposes a framework that can model the temporal bias of individual items defined by their characteristic contents, and provides a simple process for bias correction. Bias correction is done either by cleaning the bias from historical training data that is used for building predictive model, or by ignoring the estimated bias from the predictions of a standard predictor. Evaluated on two real world datasets, NetFlix and MovieLens, our framework is shown to be able to estimate and remove the bias as a result of adopted marketing techniques from the predicted popularity of items at a given time.

  19. Biasing of Capacitive Micromachined Ultrasonic Transducers.

    Science.gov (United States)

    Caliano, Giosue; Matrone, Giulia; Savoia, Alessandro Stuart

    2017-02-01

    Capacitive micromachined ultrasonic transducers (CMUTs) represent an effective alternative to piezoelectric transducers for medical ultrasound imaging applications. They are microelectromechanical devices fabricated using silicon micromachining techniques, developed in the last two decades in many laboratories. The interest for this novel transducer technology relies on its full compatibility with standard integrated circuit technology that makes it possible to integrate on the same chip the transducers and the electronics, thus enabling the realization of extremely low-cost and high-performance devices, including both 1-D or 2-D arrays. Being capacitive transducers, CMUTs require a high bias voltage to be properly operated in pulse-echo imaging applications. The typical bias supply residual ripple of high-quality high-voltage (HV) generators is in the millivolt range, which is comparable with the amplitude of the received echo signals, and it is particularly difficult to minimize. The aim of this paper is to analyze the classical CMUT biasing circuits, highlighting the features of each one, and to propose two novel HV generator architectures optimized for CMUT biasing applications. The first circuit proposed is an ultralow-residual ripple (generator that uses an extremely stable sinusoidal power oscillator topology. The second circuit employs a commercially available integrated step-up converter characterized by a particularly efficient switching topology. The circuit is used to bias the CMUT by charging a buffer capacitor synchronously with the pulsing sequence, thus reducing the impact of the switching noise on the received echo signals. The small area of the circuit (about 1.5 cm 2 ) makes it possible to generate the bias voltage inside the probe, very close to the CMUT, making the proposed solution attractive for portable applications. Measurements and experiments are shown to demonstrate the effectiveness of the new approaches presented.

  20. A theoretical model for analysing gender bias in medicine.

    Science.gov (United States)

    Risberg, Gunilla; Johansson, Eva E; Hamberg, Katarina

    2009-08-03

    During the last decades research has reported unmotivated differences in the treatment of women and men in various areas of clinical and academic medicine. There is an ongoing discussion on how to avoid such gender bias. We developed a three-step-theoretical model to understand how gender bias in medicine can occur and be understood. In this paper we present the model and discuss its usefulness in the efforts to avoid gender bias. In the model gender bias is analysed in relation to assumptions concerning difference/sameness and equity/inequity between women and men. Our model illustrates that gender bias in medicine can arise from assuming sameness and/or equity between women and men when there are genuine differences to consider in biology and disease, as well as in life conditions and experiences. However, gender bias can also arise from assuming differences when there are none, when and if dichotomous stereotypes about women and men are understood as valid. This conceptual thinking can be useful for discussing and avoiding gender bias in clinical work, medical education, career opportunities and documents such as research programs and health care policies. Too meet the various forms of gender bias, different facts and measures are needed. Knowledge about biological differences between women and men will not reduce bias caused by gendered stereotypes or by unawareness of health problems and discrimination associated with gender inequity. Such bias reflects unawareness of gendered attitudes and will not change by facts only. We suggest consciousness-rising activities and continuous reflections on gender attitudes among students, teachers, researchers and decision-makers.

  1. Validation of the AMSU-B Bias Corrections Based on Satellite Measurements from SSM/T-2

    Science.gov (United States)

    Kolodner, Marc A.

    1999-01-01

    The NOAA-15 Advanced Microwave Sounding Unit-B (AMSU-B) was designed in the same spirit as the Special Sensor Microwave Water Vapor Profiler (SSM/T-2) on board the DMSP F11-14 satellites, to perform remote sensing of spatial and temporal variations in mid and upper troposphere humidity. While the SSM/T-2 instruments have a 48 km spatial resolution at nadir and 28 beam positions per scan, AMSU-B provides an improvement with a 16 km spatial resolution at nadir and 90 beam positions per scan. The AMSU-B instrument, though, has been experiencing radio frequency interference (RFI) contamination from the NOAA-15 transmitters whose effect is dependent upon channel, geographic location, and current spacecraft antenna configuration. This has lead to large cross-track biases reaching as high as 100 Kelvin for channel 17 (150 GHz) and 50 Kelvin for channel 19 (183 +/-3 GHz). NOAA-NESDIS has recently provided a series of bias corrections for AMSU-B data starting from March, 1999. These corrections are available for each of the five channels, for every third field of view, and for three cycles within an eight second period. There is also a quality indicator in each data record to indicate whether or not the bias corrections should be applied. As a precursor to performing retrievals of mid and upper troposphere humidity, a validation study is performed by statistically analyzing the differences between the F14 SSM/T-2 and the bias corrected AMSU-B brightness temperatures for three months in the spring of 1999.

  2. EVIDENCE FOR (AND AGAINST) PROGENITOR BIAS IN THE SIZE GROWTH OF COMPACT RED GALAXIES

    Energy Technology Data Exchange (ETDEWEB)

    Keating, Stephanie K.; Abraham, Roberto G. [Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4 (Canada); Schiavon, Ricardo [Gemini Observatory, 670 North A' ohoku Place, Hilo, HI 96720 (United States); Graves, Genevieve [Department of Astronomy, University of California, Berkeley, CA 94720 (United States); Damjanov, Ivana [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Yan, Renbin [Department of Physics and Astronomy, University of Kentucky, 505 Rose Street, Lexington, KY 40506-0055 (United States); Newman, Jeffrey [Department of Physics and Astronomy, University of Pittsburgh, 3941 O' Hara Street, Pittsburgh, PA 15260 (United States); Simard, Luc [National Research Council of Canada, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada)

    2015-01-01

    Most massive, passive galaxies are compact at high redshifts, but similarly compact massive galaxies are rare in the local universe. The most common interpretation of this phenomenon is that massive galaxies have grown in size by a factor of about five since redshift z = 2. An alternative explanation is that recently quenched massive galaxies are larger (a {sup p}rogenitor bias{sup )}. In this paper, we explore the importance of progenitor bias by looking for systematic differences in the stellar populations of compact early-type galaxies in the DEEP2 survey as a function of size. Our analysis is based on applying the statistical technique of bootstrap resampling to constrain differences in the median ages of our samples and to begin to characterize the distribution of stellar populations in our co-added spectra. The light-weighted ages of compact early-type galaxies at redshifts 0.5 < z < 1.4 are compared to those of a control sample of larger galaxies at similar redshifts. We find that massive compact early-type galaxies selected on the basis of red color and high bulge-to-total ratio are younger than similarly selected larger galaxies, suggesting that size growth in these objects is not driven mainly by progenitor bias, and that individual galaxies grow as their stellar populations age. However, compact early-type galaxies selected on the basis of image smoothness and high bulge-to-total ratio are older than a control sample of larger galaxies. Progenitor bias will play a significant role in defining the apparent size changes of early-type galaxies if they are selected on the basis of the smoothness of their light distributions.

  3. Addressing Unconscious Bias: Steps toward an Inclusive Scientific Culture

    Science.gov (United States)

    Stewart, Abigail

    2011-01-01

    In this talk I will outline the nature of unconscious bias, as it operates to exclude or marginalize some participants in the scientific community. I will show how bias results from non-conscious expectations about certain groups of people, including scientists and astronomers. I will outline scientific research in psychology, sociology and economics that has identified the impact these expectations have on interpersonal judgments that are at the heart of assessment of individuals' qualifications. This research helps us understand not only how bias operates within a single instance of evaluation, but how evaluation bias can accumulate over a career if not checked, creating an appearance of confirmation of biased expectations. Some research has focused on how best to interrupt and mitigate unconscious bias, and many institutions--including the University of Michigan--have identified strategic interventions at key points of institutional decision-making (particularly hiring, annual review, and promotion) that can make a difference. The NSF ADVANCE Institutional Transformation program encouraged institutions to draw on the social science literature to create experimental approaches to addressing unconscious bias. I will outline four approaches to intervention that have arisen through the ADVANCE program: (1) systematic education that increases awareness among decisionmakers of how evaluation bias operates; (2) development of practices that mitigate the operation of bias even when it is out of conscious awareness; (3) creation of institutional policies that routinize and sanction these practices; and (4) holding leaders accountable for these implementation of these new practices and policies. Although I will focus on ways to address unconscious bias within scientific institutions (colleges and universities, laboratories and research centers, etc.), I will close by considering how scientific organizations can address unconscious bias and contribute to creating an

  4. Hemispheric biases and the control of visuospatial attention: an ERP study

    Directory of Open Access Journals (Sweden)

    Banich Marie T

    2005-08-01

    Full Text Available Abstract Background We examined whether individual differences in hemispheric utilization can interact with the intrinsic attentional biases of the cerebral hemispheres. Evidence suggests that the hemispheres have competing biases to direct attention contralaterally, with the left hemisphere (LH having a stronger bias than the right hemisphere. There is also evidence that individuals have characteristic biases to utilize one hemisphere more than the other for processing information, which can induce a bias to direct attention to contralateral space. We predicted that LH-biased individuals would display a strong rightward attentional bias, which would create difficulty in selectively attending to target stimuli in the left visual field (LVF as compared to right in the performance of a bilateral flanker task. Results Consistent with our hypothesis, flanker interference effects were found on the N2c event-related brain potential and error rate for LH-biased individuals in the Attend-LVF condition. The error rate effect was correlated with the degree of hemispheric utilization bias for the LH-Bias group. Conclusion We conclude that hemispheric utilization bias can enhance a hemisphere's contralateral attentional bias, at least for individuals with a LH utilization bias. Hemispheric utilization bias may play an important and largely unrecognized role in visuospatial attention.

  5. Attentional bias modification encourages healthy eating.

    Science.gov (United States)

    Kakoschke, Naomi; Kemps, Eva; Tiggemann, Marika

    2014-01-01

    The continual exposure to unhealthy food cues in the environment encourages poor dietary habits, in particular consuming too much fat and sugar, and not enough fruit and vegetables. According to Berridge's (2009) model of food reward, unhealthy eating is a behavioural response to biased attentional processing. The present study used an established attentional bias modification paradigm to discourage the consumption of unhealthy food and instead promote healthy eating. Participants were 146 undergraduate women who were randomly assigned to two groups: one was trained to direct their attention toward pictures of healthy food ('attend healthy' group) and the other toward unhealthy food ('attend unhealthy' group). It was found that participants trained to attend to healthy food cues demonstrated an increased attentional bias for such cues and ate relatively more of the healthy than unhealthy snacks compared to the 'attend unhealthy' group. Theoretically, the results support the postulated link between biased attentional processing and consumption (Berridge, 2009). At a practical level, they offer potential scope for interventions that focus on eating well. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Short Communication: Gender Bias and Stigmatization against ...

    African Journals Online (AJOL)

    Short Communication: Gender Bias and Stigmatization against Women Living with ... In Ethiopia, HIV/AIDS is highly stigmatized due to the fact that sexual ... bias, socio-economic situations and traditional beliefs contribute, individually and in ...

  7. Awareness and minimisation of systematic bias in research.

    LENUS (Irish Health Repository)

    Malone, Helen

    2014-03-01

    A major goal of nursing and midwifery is the delivery of evidence-based practice. Consequently, it is essential for the quality and safety of patient\\/client care that policy makers, educators and practitioners are aware of the presence of potential systematic bias in research practice and research publications so that only sound evidence translates into practice. The main aim of this paper is to highlight the need for ongoing awareness of the potential presence of systematic bias in research practice, to explore commonly reported types of systematic bias and to report some methods that can be applied to minimise systematic bias in research.

  8. Statistical analysis of extreme values from insurance, finance, hydrology and other fields

    CERN Document Server

    Reiss, Rolf-Dieter

    1997-01-01

    The statistical analysis of extreme data is important for various disciplines, including hydrology, insurance, finance, engineering and environmental sciences. This book provides a self-contained introduction to the parametric modeling, exploratory analysis and statistical interference for extreme values. The entire text of this third edition has been thoroughly updated and rearranged to meet the new requirements. Additional sections and chapters, elaborated on more than 100 pages, are particularly concerned with topics like dependencies, the conditional analysis and the multivariate modeling of extreme data. Parts I–III about the basic extreme value methodology remain unchanged to some larger extent, yet notable are, e.g., the new sections about "An Overview of Reduced-Bias Estimation" (co-authored by M.I. Gomes), "The Spectral Decomposition Methodology", and "About Tail Independence" (co-authored by M. Frick), and the new chapter about "Extreme Value Statistics of Dependent Random Variables" (co-authored ...

  9. Cognitive bias in action: evidence for a reciprocal relation between confirmation bias and fear in children.

    Science.gov (United States)

    Remmerswaal, Danielle; Huijding, Jorg; Bouwmeester, Samantha; Brouwer, Marlies; Muris, Peter

    2014-03-01

    Some cognitive models propose that information processing biases and fear are reciprocally related. This idea has never been formally tested. Therefore, this study investigated the existence of a vicious circle by which confirmation bias and fear exacerbate each other. One-hundred-and-seventy-one school children (8-13 years) were first provided with threatening, ambiguous, or positive information about an unknown animal. Then they completed a computerized information search task during which they could collect additional (negative, positive, or neutral) information about the novel animal. Because fear levels were repeatedly assessed during the task, it was possible to examine the reciprocal relationship between confirmation bias and fear. A reciprocal relation of mutual reinforcement was found between confirmation bias and fear over the course of the experiment: increases in fear predicted subsequent increases in the search for negative information, and increases in the search for negative information further enhanced fear on a later point-in-time. In addition, the initial information given about the animals successfully induced diverging fear levels in the children, and determined their first inclination to search for additional information. As this study employed a community sample of primary school children, future research should test whether these results can be generalized to clinically anxious youth. These findings provide first support for the notion that fearful individuals may become trapped in a vicious circle in which fear and a fear-related confirmation bias mutually strengthen each other, thereby maintaining the anxiety pathology. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Mood-congruent free recall bias in anxious individuals is not a consequence of response bias

    OpenAIRE

    Russo, Riccardo; Whittuck, Dora; Roberson, Debi; Dutton, Kevin; Georgiou, George; Fox, Elaine

    2006-01-01

    The status of mood-congruent free recall bias in anxious individuals was evaluated following incidental encoding of target words. Individuals with high and low levels of trait anxiety completed a modified Stroop task, which revealed an attentional bias for threat-related stimuli in anxious individuals. This group was significantly slower in naming the colour in which threat-related words were displayed compared to neutral words. In a subsequent free recall test for the words used in the modif...

  11. Gender Bias: Inequities in the Classroom.

    Science.gov (United States)

    Love, Reeve

    1993-01-01

    This article explores sex bias in curricular materials for elementary and secondary schools. Sex bias is defined as a set of unconscious behaviors that, in themselves, are often trivial and generally favorable. Although these behaviors do not hurt if they happen only once, they can cause a great deal of harm if a pattern develops that serves to…

  12. Sources of bias in clinical ethics case deliberation.

    Science.gov (United States)

    Magelssen, Morten; Pedersen, Reidar; Førde, Reidun

    2014-10-01

    A central task for clinical ethics consultants and committees (CEC) is providing analysis of, and advice on, prospective or retrospective clinical cases. However, several kinds of biases may threaten the integrity, relevance or quality of the CEC's deliberation. Bias should be identified and, if possible, reduced or counteracted. This paper provides a systematic classification of kinds of bias that may be present in a CEC's case deliberation. Six kinds of bias are discussed, with examples, as to their significance and risk factors. Possible remedies are suggested. The potential for bias is greater when the case deliberation is performed by an individual ethics consultant than when an entire clinical ethics committee is involved. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  13. Approach bias for food cues in obese individuals.

    Science.gov (United States)

    Kemps, Eva; Tiggemann, Marika

    2015-01-01

    This study aimed to investigate the existence of an approach bias for food cues in obese individuals. A community sample of 56 obese women and 56 normal weight controls completed an approach-avoidance variant of the implicit association task. The obese participants were faster to respond to trials that paired food words with approach words, and trials that paired non-food words with avoid words, than the converse pairings, thus, demonstrating an approach bias for food. This bias was evident for both high caloric and low caloric food words, and was not attributable to a state of deprivation or feelings of hunger. By contrast, the normal weight controls did not show any such bias. The results are consistent with recent neurocognitive perspectives of obesity. At a practical level, approach biases for food may present a potential target for modifying (excessive) food intake.

  14. Cognitive bias in forensic anthropology: visual assessment of skeletal remains is susceptible to confirmation bias.

    Science.gov (United States)

    Nakhaeizadeh, Sherry; Dror, Itiel E; Morgan, Ruth M

    2014-05-01

    An experimental study was designed to examine cognitive biases within forensic anthropological non-metric methods in assessing sex, ancestry and age at death. To investigate examiner interpretation, forty-one non-novice participants were semi randomly divided into three groups. Prior to conducting the assessment of the skeletal remains, two of the groups were given different extraneous contextual information regarding the sex, ancestry and age at death of the individual. The third group acted as a control group with no extraneous contextual information. The experiment was designed to investigate if the interpretation and conclusions of the skeletal remains would differ amongst participants within the three groups, and to assess whether the examiners would confirm or disagree with the given extraneous context when establishing a biological profile. The results revealed a significant biasing effect within the three groups, demonstrating a strong confirmation bias in the assessment of sex, ancestry and age at death. In assessment of sex, 31% of the participants in the control group concluded that the skeleton remains were male. In contrast, in the group that received contextual information that the remains were male, 72% concluded that the remains were male, and in the participant group where the context was that the remains were of a female, 0% of the participants concluded that the remains were male. Comparable results showing bias were found in assessing ancestry and age at death. These data demonstrate that cognitive bias can impact forensic anthropological non-metric methods on skeletal remains and affects the interpretation and conclusions of the forensic scientists. This empirical study is a step in establishing an evidence base approach for dealing with cognitive issues in forensic anthropological assessments, so as to enhance this valuable forensic science discipline. Copyright © 2013 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights

  15. News Consumption and Media Bias

    OpenAIRE

    Yi Xiang; Miklos Sarvary

    2007-01-01

    Bias in the market for news is well-documented. Recent research in economics explains the phenomenon by assuming that consumers want to read (watch) news that is consistent with their tastes or prior beliefs rather than the truth. The present paper builds on this idea but recognizes that (i) besides “biased” consumers, there are also “conscientious” consumers whose sole interest is in discovering the truth, and (ii) consistent with reality, media bias is constrained by the truth. These two fa...

  16. Specificity and overlap of attention and memory biases in depression.

    Science.gov (United States)

    Marchetti, Igor; Everaert, Jonas; Dainer-Best, Justin; Loeys, Tom; Beevers, Christopher G; Koster, Ernst H W

    2018-01-01

    Attentional and memory biases are viewed as crucial cognitive processes underlying symptoms of depression. However, it is still unclear whether these two biases are uniquely related to depression or whether they show substantial overlap. We investigated the degree of specificity and overlap of attentional and memory biases for depressotypic stimuli in relation to depression and anxiety by means of meta-analytic commonality analysis. By including four published studies, we considered a pool of 463 healthy and subclinically depressed individuals, different experimental paradigms, and different psychological measures. Memory bias is reliably and strongly related to depression and, specifically, to symptoms of negative mood, worthlessness, feelings of failure, and pessimism. Memory bias for negative information was minimally related to anxiety. Moreover, neither attentional bias nor the overlap between attentional and memory biases were significantly related to depression. Limitations include cross-sectional nature of the study. Our study showed that, across different paradigms and psychological measures, memory bias (and not attentional bias) represents a primary mechanism in depression. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Correcting the bias of empirical frequency parameter estimators in codon models.

    Directory of Open Access Journals (Sweden)

    Sergei Kosakovsky Pond

    2010-07-01

    Full Text Available Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a "corrected" empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators.

  18. Application of extended statistical combination of uncertainties methodology for digital nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    In, Wang Ki; Uh, Keun Sun; Chul, Kim Heui [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1995-02-01

    A technically more direct statistical combinations of uncertainties methodology, extended SCU (XSCU), was applied to statistically combine the uncertainties associated with the DNBR alarm setpoint and the DNBR trip setpoint of digital nuclear power plants. The modified SCU (MSCU) methodology is currently used as the USNRC approved design methodology to perform the same function. In this report, the MSCU and XSCU methodologies were compared in terms of the total uncertainties and the net margins to the DNBR alarm and trip setpoints. The MSCU methodology resulted in the small total penalties due to a significantly negative bias which are quite large. However the XSCU methodology gave the virtually unbiased total uncertainties. The net margins to the DNBR alarm and trip setpoints by the MSCU methodology agree with those by the XSCU methodology within statistical variations. (Author) 12 refs., 17 figs., 5 tabs.

  19. A theoretical model for analysing gender bias in medicine

    Directory of Open Access Journals (Sweden)

    Johansson Eva E

    2009-08-01

    Full Text Available Abstract During the last decades research has reported unmotivated differences in the treatment of women and men in various areas of clinical and academic medicine. There is an ongoing discussion on how to avoid such gender bias. We developed a three-step-theoretical model to understand how gender bias in medicine can occur and be understood. In this paper we present the model and discuss its usefulness in the efforts to avoid gender bias. In the model gender bias is analysed in relation to assumptions concerning difference/sameness and equity/inequity between women and men. Our model illustrates that gender bias in medicine can arise from assuming sameness and/or equity between women and men when there are genuine differences to consider in biology and disease, as well as in life conditions and experiences. However, gender bias can also arise from assuming differences when there are none, when and if dichotomous stereotypes about women and men are understood as valid. This conceptual thinking can be useful for discussing and avoiding gender bias in clinical work, medical education, career opportunities and documents such as research programs and health care policies. Too meet the various forms of gender bias, different facts and measures are needed. Knowledge about biological differences between women and men will not reduce bias caused by gendered stereotypes or by unawareness of health problems and discrimination associated with gender inequity. Such bias reflects unawareness of gendered attitudes and will not change by facts only. We suggest consciousness-rising activities and continuous reflections on gender attitudes among students, teachers, researchers and decision-makers.

  20. Editorial Bias in Crowd-Sourced Political Information.

    Directory of Open Access Journals (Sweden)

    Joshua L Kalla

    Full Text Available The Internet has dramatically expanded citizens' access to and ability to engage with political information. On many websites, any user can contribute and edit "crowd-sourced" information about important political figures. One of the most prominent examples of crowd-sourced information on the Internet is Wikipedia, a free and open encyclopedia created and edited entirely by users, and one of the world's most accessed websites. While previous studies of crowd-sourced information platforms have found them to be accurate, few have considered biases in what kinds of information are included. We report the results of four randomized field experiments that sought to explore what biases exist in the political articles of this collaborative website. By randomly assigning factually true but either positive or negative and cited or uncited information to the Wikipedia pages of U.S. senators, we uncover substantial evidence of an editorial bias toward positivity on Wikipedia: Negative facts are 36% more likely to be removed by Wikipedia editors than positive facts within 12 hours and 29% more likely within 3 days. Although citations substantially increase an edit's survival time, the editorial bias toward positivity is not eliminated by inclusion of a citation. We replicate this study on the Wikipedia pages of deceased as well as recently retired but living senators and find no evidence of an editorial bias in either. Our results demonstrate that crowd-sourced information is subject to an editorial bias that favors the politically active.

  1. Editorial Bias in Crowd-Sourced Political Information.

    Science.gov (United States)

    Kalla, Joshua L; Aronow, Peter M

    2015-01-01

    The Internet has dramatically expanded citizens' access to and ability to engage with political information. On many websites, any user can contribute and edit "crowd-sourced" information about important political figures. One of the most prominent examples of crowd-sourced information on the Internet is Wikipedia, a free and open encyclopedia created and edited entirely by users, and one of the world's most accessed websites. While previous studies of crowd-sourced information platforms have found them to be accurate, few have considered biases in what kinds of information are included. We report the results of four randomized field experiments that sought to explore what biases exist in the political articles of this collaborative website. By randomly assigning factually true but either positive or negative and cited or uncited information to the Wikipedia pages of U.S. senators, we uncover substantial evidence of an editorial bias toward positivity on Wikipedia: Negative facts are 36% more likely to be removed by Wikipedia editors than positive facts within 12 hours and 29% more likely within 3 days. Although citations substantially increase an edit's survival time, the editorial bias toward positivity is not eliminated by inclusion of a citation. We replicate this study on the Wikipedia pages of deceased as well as recently retired but living senators and find no evidence of an editorial bias in either. Our results demonstrate that crowd-sourced information is subject to an editorial bias that favors the politically active.

  2. A systematic review of context bias in invasion biology.

    Directory of Open Access Journals (Sweden)

    Robert J Warren

    Full Text Available The language that scientists use to frame biological invasions may reveal inherent bias-including how data are interpreted. A frequent critique of invasion biology is the use of value-laden language that may indicate context bias. Here we use a systematic study of language and interpretation in papers drawn from invasion biology to evaluate whether there is a link between the framing of papers and the interpretation of results. We also examine any trends in context bias in biological invasion research. We examined 651 peer-reviewed invasive species competition studies and implemented a rigorous systematic review to examine bias in the presentation and interpretation of native and invasive competition in invasion biology. We predicted that bias in the presentation of invasive species is increasing, as suggested by several authors, and that bias against invasive species would result in misinterpreting their competitive dominance in correlational observational studies compared to causative experimental studies. We indeed found evidence of bias in the presentation and interpretation of invasive species research; authors often introduced research with invasive species in a negative context and study results were interpreted against invasive species more in correlational studies. However, we also found a distinct decrease in those biases since the mid-2000s. Given that there have been several waves of criticism from scientists both inside and outside invasion biology, our evidence suggests that the subdiscipline has somewhat self-corrected apparent biases.

  3. A systematic review of context bias in invasion biology.

    Science.gov (United States)

    Warren, Robert J; King, Joshua R; Tarsa, Charlene; Haas, Brian; Henderson, Jeremy

    2017-01-01

    The language that scientists use to frame biological invasions may reveal inherent bias-including how data are interpreted. A frequent critique of invasion biology is the use of value-laden language that may indicate context bias. Here we use a systematic study of language and interpretation in papers drawn from invasion biology to evaluate whether there is a link between the framing of papers and the interpretation of results. We also examine any trends in context bias in biological invasion research. We examined 651 peer-reviewed invasive species competition studies and implemented a rigorous systematic review to examine bias in the presentation and interpretation of native and invasive competition in invasion biology. We predicted that bias in the presentation of invasive species is increasing, as suggested by several authors, and that bias against invasive species would result in misinterpreting their competitive dominance in correlational observational studies compared to causative experimental studies. We indeed found evidence of bias in the presentation and interpretation of invasive species research; authors often introduced research with invasive species in a negative context and study results were interpreted against invasive species more in correlational studies. However, we also found a distinct decrease in those biases since the mid-2000s. Given that there have been several waves of criticism from scientists both inside and outside invasion biology, our evidence suggests that the subdiscipline has somewhat self-corrected apparent biases.

  4. Body image related negative interpretation bias in anorexia nervosa.

    Science.gov (United States)

    Brockmeyer, Timo; Anderle, Alisa; Schmidt, Hagen; Febry, Stephanie; Wünsch-Leiteritz, Wally; Leiteritz, Andreas; Friederich, Hans-Christoph

    2018-05-01

    A distorted body image and pronounced body dissatisfaction are hallmarks of anorexia nervosa (AN) that typically result in dietary restraint and compensatory behaviours. Cognitive biases such as negative interpretation bias are considered key maintaining factors of these maladaptive cognitions and behaviours. However, little attention has been paid to empirical tests whether negative interpretation bias exists in AN and to what degree it is associated with symptom severity. Participants in the present study were 40 women with AN and 40 healthy women with no history of an eating disorder. Body-related negative interpretation bias (i.e., a tendency to interpret ambiguous information about the own body in a negative way) was measured by a Scrambled Sentences Task. Patients with AN showed a stronger body-related negative interpretation bias than healthy controls. Within both groups, negative interpretation bias correlated strongly and positively with AN symptom severity and these effects were not moderated by levels of depressive symptoms. The findings support the idea that biased interpretation of body-related information is associated with the specific psychopathology of AN. Targeted, computerised interventions (e.g. interpretation bias modification) may help to alter these dysfunctional cognitive schemas that lie at the heart of AN. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Multilayer DLC coatings via alternating bias during magnetron sputtering

    Energy Technology Data Exchange (ETDEWEB)

    Li Fengji [School of Mechanical and Aerospace Engineering, Nanyang Technological University (Singapore); Zhang, Sam, E-mail: msyzhang@ntu.edu.sg [School of Mechanical and Aerospace Engineering, Nanyang Technological University (Singapore); Kong Junhua [School of Materials Science and Engineering, Nanyang Technological University (Singapore); Zhang Yujuan [Key Laboratory of Special Functional Material, Henan University (China); Zhang Wali [School of Mechanical and Aerospace Engineering, Nanyang Technological University (Singapore)

    2011-05-31

    To combat the high residual stress problem in monolayer diamond-like carbon coatings, this paper fabricated multilayer diamond-like carbon coatings with alternate soft and hard layers via alternating bias during magnetron sputtering. The surface, cross sectional morphology, bonding structures and mechanical properties are investigated. The atomic force microscopy images indicate low bias results in rougher surface with large graphite clusters and voids suggesting low coating density. The multilayered coatings demonstrate relatively smooth surface stemming from higher bias. The cross sectional images from field emission scanning electron microscopy indicate coating thickness decreases as substrate bias increases and confirm that higher bias results in denser coating. Delamination is observed in monolayer coatings due to high residual stress. The trend of sp{sup 3}/sp{sup 2} fraction estimated by X-ray photoelectron spectroscopy is consistent with that of I{sub D}/I{sub G} ratios from Raman spectra, indicating the change of bonding structure with change of substrate bias. Hardness of multilayer diamond-like carbon coating is comparable to the coatings deposited at low constant bias but the adhesion strength and toughness are significantly improved. Alternately biased sputtering deposition provides an alternative when combination of hardness, toughness and adhesion strength is needed in an all diamond-like carbon coating.

  6. Decision-making heuristics and biases across the life span.

    Science.gov (United States)

    Strough, Jonell; Karns, Tara E; Schlosnagle, Leo

    2011-10-01

    We outline a contextual and motivational model of judgment and decision-making (JDM) biases across the life span. Our model focuses on abilities and skills that correspond to deliberative, experiential, and affective decision-making processes. We review research that addresses links between JDM biases and these processes as represented by individual differences in specific abilities and skills (e.g., fluid and crystallized intelligence, executive functioning, emotion regulation, personality traits). We focus on two JDM biases-the sunk-cost fallacy (SCF) and the framing effect. We trace the developmental trajectory of each bias from preschool through middle childhood, adolescence, early adulthood, and later adulthood. We conclude that life-span developmental trajectories differ depending on the bias investigated. Existing research suggests relative stability in the framing effect across the life span and decreases in the SCF with age, including in later life. We highlight directions for future research on JDM biases across the life span, emphasizing the need for process-oriented research and research that increases our understanding of JDM biases in people's everyday lives. © 2011 New York Academy of Sciences.

  7. Cognitive bias in symptomatic and recovered agoraphobics.

    Science.gov (United States)

    Stoler, L S; McNally, R J

    1991-01-01

    Symptomatic agoraphobics, recovered agoraphobics, and normal control subjects completed a series of sentence stems that had either ambiguous or unambiguous meanings, and had either a potentially threatening or a nonthreatening connotation. The written completions made by subjects to these stems were classified as indicating either a biased (i.e. threat-related) or unbiased interpretation of the meaning of the stem, and if a biased interpretation was made, whether the subject indicated efforts at adaptive coping with the perceived threat. Results indicated that symptomatic agoraphobics exhibited strong biases for interpreting information as threatening, relative to normal control subjects. Moreover, recovered agoraphobics resembled symptomatic agoraphobics more than normal control subjects, thus indicating that cognitive biases may persist following cessation of panic attacks and reductions in avoidance behavior. However, recovered agoraphobics also exhibited tendencies to cope adaptively with perceived threats whereas symptomatic agoraphobics did not.

  8. High-Q perpendicular-biased ferrite-tuned cavity

    International Nuclear Information System (INIS)

    Carlini, R.D.; Thiessen, H.A.; Potter, J.M.

    1983-01-01

    Rapid-cycling proton synchrotrons, such as the proposed LAMPF II accelerator, require approximately 10 MV per turn rf with 17% tuning range near 50 MHz. The traditional approach to ferrite-tuned cavities uses a ferrite which is longitudinally biased (rf magnetic field parallel to bias field). This method leads to unacceptably high losses in the ferrite. At Los Alamos, we are developing a cavity with transverse bias (rf magnetic field perpendicular to the bias field) that makes use of the tensor permeability of the ferrite. Modest power tests of a small (10-cm-dia) quarter-wave singly re-entrant cavity tuned by nickel-zinc ferrites and aluminum-doped garnets indicate that the losses in the ferrite can be made negligible compared with the losses due to the surface resistivity of the copper cavity at power levels from 2 to 200 watts

  9. Monte Carlo shielding analyses using an automated biasing procedure

    International Nuclear Information System (INIS)

    Tang, J.S.; Hoffman, T.J.

    1988-01-01

    A systematic and automated approach for biasing Monte Carlo shielding calculations is described. In particular, adjoint fluxes from a one-dimensional discrete ordinates calculation are used to generate biasing parameters for a Monte Carlo calculation. The entire procedure of adjoint calculation, biasing parameters generation, and Monte Carlo calculation has been automated. The automated biasing procedure has been applied to several realistic deep-penetration shipping cask problems. The results obtained for neutron and gamma-ray transport indicate that with the automated biasing procedure Monte Carlo shielding calculations of spent-fuel casks can be easily performed with minimum effort and that accurate results can be obtained at reasonable computing cost

  10. Reducing status quo bias in choice experiments

    DEFF Research Database (Denmark)

    Bonnichsen, Ole; Ladenburg, Jacob

    In stated preference literature, the tendency to choose the alternative representing the status quo situation seems to exceed real life status quo effects. Accordingly, status quo bias can be a problem. In Choice Experiments, status quo bias is found to be strongly correlated with protest attitudes...... toward the cost attribute. If economic values are to be elicited, this problem is difficult to remedy. In a split sample framework we test a novel ex-ante entreaty aimed specifically at the cost attribute and find that it effectively reduces status quo bias and improves the internal validity...

  11. Biased agonism of the calcium-sensing receptor

    DEFF Research Database (Denmark)

    Thomsen, Alex Rojas Bie; Hvidtfeldt, Maja; Bräuner-Osborne, Hans

    2012-01-01

    After the discovery of molecules modulating G protein-coupled receptors (GPCRs) that are able to selectively affect one signaling pathway over others for a specific GPCR, thereby "biasing" the signaling, it has become obvious that the original model of GPCRs existing in either an "on" or "off...... through recruitment of ß-arrestins. Next, by measuring activity of all three signaling pathways we found that barium, spermine, neomycin, and tobramycin act as biased agonist in terms of efficacy and/or potency. Finally, polyamines and aminoglycosides in general were biased in their potencies toward ERK1...

  12. On the Borders of Harmful and Helpful Beauty Biases

    OpenAIRE

    Maria Agthe; Maria Strobel; Matthias Spörrle; Michaela Pfundmair; Jon K. Maner

    2016-01-01

    Research with European Caucasian samples demonstrates that attractiveness-based biases in social evaluation depend on the constellation of the sex of the evaluator and the sex of the target: Whereas people generally show positive biases toward attractive opposite-sex persons, they show less positive or even negative biases toward attractive same-sex persons. By examining these biases both within and between different ethnicities, the current studies provide new evidence for both the generaliz...

  13. OBJECTS IN KEPLER'S MIRROR MAY BE LARGER THAN THEY APPEAR: BIAS AND SELECTION EFFECTS IN TRANSITING PLANET SURVEYS

    International Nuclear Information System (INIS)

    Gaidos, Eric; Mann, Andrew W.

    2013-01-01

    Statistical analyses of large surveys for transiting planets such as the Kepler mission must account for systematic errors and biases. Transit detection depends not only on the planet's radius and orbital period, but also on host star properties. Thus, a sample of stars with transiting planets may not accurately represent the target population. Moreover, targets are selected using criteria such as a limiting apparent magnitude. These selection effects, combined with uncertainties in stellar radius, lead to biases in the properties of transiting planets and their host stars. We quantify possible biases in the Kepler survey. First, Eddington bias produced by a steep planet radius distribution and uncertainties in stellar radius results in a 15%-20% overestimate of planet occurrence. Second, the magnitude limit of the Kepler target catalog induces Malmquist bias toward large, more luminous stars and underestimation of the radii of about one-third of candidate planets, especially those larger than Neptune. Third, because metal-poor stars are smaller, stars with detected planets will be very slightly (<0.02 dex) more metal-poor than the target average. Fourth, uncertainties in stellar radii produce correlated errors in planet radius and stellar irradiation. A previous finding, that highly irradiated giants are more likely to have 'inflated' radii, remains significant, even accounting for this effect. In contrast, transit depth is negatively correlated with stellar metallicity even in the absence of any intrinsic correlation, and a previous claim of a negative correlation between giant planet transit depth and stellar metallicity is probably an artifact.

  14. Ironic effects of racial bias during interracial interactions.

    Science.gov (United States)

    Shelton, J Nicole; Richeson, Jennifer A; Salvatore, Jessica; Trawalter, Sophie

    2005-05-01

    Previous research has suggested that Blacks like White interaction partners who make an effort to appear unbiased more than those who do not. We tested the hypothesis that, ironically, Blacks perceive White interaction partners who are more racially biased more positively than less biased White partners, primarily because the former group must make more of an effort to control racial bias than the latter. White participants in this study completed the Implicit Association Test (IAT) as a measure of racial bias and then discussed race relations with either a White or a Black partner. Whites' IAT scores predicted how positively they were perceived by Black (but not White) interaction partners, and this relationship was mediated by Blacks' perceptions of how engaged the White participants were during the interaction. We discuss implications of the finding that Blacks may, ironically, prefer to interact with highly racially biased Whites, at least in short interactions.

  15. Skill-Biased Technological Change in Denmark

    DEFF Research Database (Denmark)

    Malchow-Møller, Nikolaj; Rose Skaksen, Jan

    2003-01-01

    Skill-Biased Technological Change in Denmark:A Disaggregate Perspective@*In this paper, we provide an industry-level analysis of skill-biased technological change(SBTC) in Denmark over the last two decades. The analysis shows that SBTC has variedconsiderably across industries, and traditionally...... large Danish industries have experiencedrelatively less SBTC. This may partly explain why wage inequality between skilled and lessskilled has risen less in Denmark than in other countries. We also find that SBTC has beenconcentrated in already skill-intensive industries. This contains important...... information aboutfuture labour requirements, as the relative importance of these industries must be expectedto grow, thereby reinforcing the shift in demand for skilled labour.JEL Classification: J24, J31, L6Keywords: skill-biased technological change, Danish industries...

  16. Dialogue Games for Inconsistent and Biased Information

    NARCIS (Netherlands)

    Lebbink, H.J.; Witteman, C.L.M.; Meyer, J.J.C.

    2003-01-01

    In this article, a dialogue game is presented in which coherent conversational sequences with inconsistent and biased information are described at the speech act level. Inconsistent and biased information is represented with bilattice structures, and based on these bilattice structures, a

  17. Spatial statistics of pitting corrosion patterning: Quadrat counts and the non-homogeneous Poisson process

    International Nuclear Information System (INIS)

    Lopez de la Cruz, J.; Gutierrez, M.A.

    2008-01-01

    This paper presents a stochastic analysis of spatial point patterns as effect of localized pitting corrosion. The Quadrat Counts method is studied with two empirical pit patterns. The results are dependent on the quadrat size and bias is introduced when empty quadrats are accounted for the analysis. The spatially inhomogeneous Poisson process is used to improve the performance of the Quadrat Counts method. The latter combines Quadrat Counts with distance-based statistics in the analysis of pit patterns. The Inter-Event and the Nearest-Neighbour statistics are here implemented in order to compare their results. Further, the treatment of patterns in irregular domains is discussed

  18. Selection bias and the perils of benchmarking.

    Science.gov (United States)

    Denrell, Jerker

    2005-04-01

    To find the secrets of business success, what could be more natural than studying successful businesses? In fact, nothing could be more dangerous, warns this Stanford professor. Generalizing from the examples of successful companies is like generalizing about New England weather from data taken only in the summer. That's essentially what businesspeople do when they learn from good examples and what consultants, authors, and researchers do when they study only existing companies or--worse yet--only high-performing companies. They reach conclusions from unrepresentative data samples, falling into the classic statistical trap of selection bias. Drawing on a wealth of case studies, for instance, one researcher concluded that great leaders share two key traits: They persist, often despite initial failures, and they are able to persuade others to join them. But those traits are also the hallmarks of spectacularly unsuccessful entrepreneurs, who must persist in the face of failure to incur large losses and must be able to persuade others to pour their money down the drain. To discover what makes a business successful, then, managers should look at both successes and failures. Otherwise, they will overvalue risky business practices, seeing only those companies that won big and not the ones that lost dismally. They will not be able to tell if their current good fortune stems from smart business practices or if they are actually coasting on past accomplishments or good luck. Fortunately, economists have developed relatively simple tools that can correct for selection bias even when data about failed companies are hard to come by. Success may be inspirational, but managers are more likely to find the secrets of high performance if they give the stories of their competitors'failures as full a hearing as they do the stories of dazzling successes.

  19. Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data

    Science.gov (United States)

    Hu, Ming; Deng, Ke; Qin, Zhaohui; Liu, Jun S.

    2015-01-01

    Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research. PMID:26124977

  20. Level set segmentation of medical images based on local region statistics and maximum a posteriori probability.

    Science.gov (United States)

    Cui, Wenchao; Wang, Yi; Lei, Tao; Fan, Yangyu; Feng, Yan

    2013-01-01

    This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

  1. The lighter side of advertising: investigating posing and lighting biases.

    Science.gov (United States)

    Thomas, Nicole A; Burkitt, Jennifer A; Patrick, Regan E; Elias, Lorin J

    2008-11-01

    People tend to display the left cheek when posing for a portrait; however, this effect does not appear to generalise to advertising. The amount of body visible in the image and the sex of the poser might also contribute to the posing bias. Portraits also exhibit lateral lighting biases, with most images being lit from the left. This effect might also be present in advertisements. A total of 2801 full-page advertisements were sampled and coded for posing direction, lighting direction, sex of model, and amount of body showing. Images of females showed an overall leftward posing bias, but the biases in males depended on the amount of body visible. Males demonstrated rightward posing biases for head-only images. Overall, images tended to be lit from the top left corner. The two factors of posing and lighting biases appear to influence one another. Leftward-lit images had more leftward poses than rightward, while the opposite occurred for rightward-lit images. Collectively, these results demonstrate that the posing biases in advertisements are dependent on the amount of body showing in the image, and that biases in lighting direction interact with these posing biases.

  2. Imaging the neural effects of cognitive bias modification training

    NARCIS (Netherlands)

    Wiers, C.E.; Wiers, R.W.

    Cognitive bias modification (CBM) was first developed as an experimental tool to examine the causal role of cognitive biases, and later developed into complementary interventions in experimental psychopathology research. CBM involves the "re-training" of implicit biases by means of multiple trials

  3. Taphonomic trade-offs in tropical marine death assemblages: Differential time averaging, shell loss, and probable bias in siliciclastic vs. carbonate facies

    Science.gov (United States)

    Kidwell, Susan M.; Best, Mairi M. R.; Kaufman, Darrell S.

    2005-09-01

    Radiocarbon-calibrated amino-acid racemization ages of individually dated bivalve mollusk shells from Caribbean reef, nonreefal carbonate, and siliciclastic sediments in Panama indicate that siliciclastic sands and muds contain significantly older shells (median 375 yr, range up to ˜5400 yr) than nearby carbonate seafloors (median 72 yr, range up to ˜2900 yr; maximum shell ages differ significantly at p < 0.02 using extreme-value statistics). The implied difference in shell loss rates is contrary to physicochemical expectations but is consistent with observed differences in shell condition (greater bioerosion and dissolution in carbonates). Higher rates of shell loss in carbonate sediments should lead to greater compositional bias in surviving skeletal material, resulting in taphonomic trade-offs: less time averaging but probably higher taxonomic bias in pure carbonate sediments, and lower bias but greater time averaging in siliciclastic sediments from humid-weathered accretionary arc terrains, which are a widespread setting of tropical sedimentation.

  4. Importance biasing quality criterion based on contribution response theory

    International Nuclear Information System (INIS)

    Borisov, N.M.; Panin, M.P.

    2001-01-01

    The report proposes a visual criterion of importance biasing both of forward and adjoint simulation. The similarity of contribution Monte Carlo and importance biasing random collision event distribution is proved. The conservation of total number of random trajectory crossings of surfaces, which separate the source and the detector is proposed as importance biasing quality criterion. The use of this criterion is demonstrated on the example of forward vs. adjoint importance biasing in gamma ray deep penetration problem. The larger amount of published data on forward field characteristics than on adjoint leads to the more accurate approximation of adjoint importance function in comparison to forward, for it adjoint importance simulation is more effective than forward. The proposed criterion indicates it visually, showing the most uniform distribution of random trajectory crossing events for the most effective importance biasing parameters and pointing to the direction of tuning importance biasing parameters. (orig.)

  5. Mutational Biases and GC-Biased Gene Conversion Affect GC Content in the Plastomes of Dendrobium Genus

    Directory of Open Access Journals (Sweden)

    Zhitao Niu

    2017-11-01

    Full Text Available The variation of GC content is a key genome feature because it is associated with fundamental elements of genome organization. However, the reason for this variation is still an open question. Different kinds of hypotheses have been proposed to explain the variation of GC content during genome evolution. However, these hypotheses have not been explicitly investigated in whole plastome sequences. Dendrobium is one of the largest genera in the orchid species. Evolutionary studies of the plastomic organization and base composition are limited in this genus. In this study, we obtained the high-quality plastome sequences of D. loddigesii and D. devonianum. The comparison results showed a nearly identical organization in Dendrobium plastomes, indicating that the plastomic organization is highly conserved in Dendrobium genus. Furthermore, the impact of three evolutionary forces—selection, mutational biases, and GC-biased gene conversion (gBGC—on the variation of GC content in Dendrobium plastomes was evaluated. Our results revealed: (1 consistent GC content evolution trends and mutational biases in single-copy (SC and inverted repeats (IRs regions; and (2 that gBGC has influenced the plastome-wide GC content evolution. These results suggest that both mutational biases and gBGC affect GC content in the plastomes of Dendrobium genus.

  6. Mutational Biases and GC-Biased Gene Conversion Affect GC Content in the Plastomes of Dendrobium Genus

    Science.gov (United States)

    Niu, Zhitao; Xue, Qingyun; Wang, Hui; Xie, Xuezhu; Zhu, Shuying; Liu, Wei; Ding, Xiaoyu

    2017-01-01

    The variation of GC content is a key genome feature because it is associated with fundamental elements of genome organization. However, the reason for this variation is still an open question. Different kinds of hypotheses have been proposed to explain the variation of GC content during genome evolution. However, these hypotheses have not been explicitly investigated in whole plastome sequences. Dendrobium is one of the largest genera in the orchid species. Evolutionary studies of the plastomic organization and base composition are limited in this genus. In this study, we obtained the high-quality plastome sequences of D. loddigesii and D. devonianum. The comparison results showed a nearly identical organization in Dendrobium plastomes, indicating that the plastomic organization is highly conserved in Dendrobium genus. Furthermore, the impact of three evolutionary forces—selection, mutational biases, and GC-biased gene conversion (gBGC)—on the variation of GC content in Dendrobium plastomes was evaluated. Our results revealed: (1) consistent GC content evolution trends and mutational biases in single-copy (SC) and inverted repeats (IRs) regions; and (2) that gBGC has influenced the plastome-wide GC content evolution. These results suggest that both mutational biases and gBGC affect GC content in the plastomes of Dendrobium genus. PMID:29099062

  7. Systematic Biases in Weak Lensing Cosmology with the Dark Energy Survey

    Energy Technology Data Exchange (ETDEWEB)

    Samuroff, Simon [Manchester U.

    2017-01-01

    impact to be subdominant to statistical error at the! current level of precision. Another major uncertainity in shear cosmology is the accuracy of our ensemble redshift distributions. Chapter 5 presents a numerical investigation into the combined constraining power of cosmic shear, galaxy clustering and their cross-correlation in DES Y1, and the potential for internal calibration of redshift errors. Introducing a moderate uniform bias into the redshift distributions used to model the weak lensing (WL) galaxies is shown to produce a $> 2\\sigma$ bias in $S_8$. We demonstrate that this cosmological bias can be eliminated by marginalising over redshift error nuisance parameters. Strikingly, the cosmological constraint of the combined dataset is largely undiminished by the loss of prior information on the WL distributions. We demonstrate that this implicit self-calibration is the result of complementary degeneracy directions in the combined data. In Chapter 6 we present the preliminary results of an investigation into galaxy intrin sic alignments. Using the DES Y1 data, we show a clear dependence in alignment amplitude on galaxy type, in agreement with previous results. We subject these findings to a series of initial robustness tests. We conclude with a short overview of the work presented, and discuss prospects for the future.

  8. Investigating vulnerability to eating disorders: biases in emotional processing.

    Science.gov (United States)

    Pringle, A; Harmer, C J; Cooper, M J

    2010-04-01

    Biases in emotional processing and cognitions about the self are thought to play a role in the maintenance of eating disorders (EDs). However, little is known about whether these difficulties exist pre-morbidly and how they might contribute to risk. Female dieters (n=82) completed a battery of tasks designed to assess the processing of social cues (facial emotion recognition), cognitions about the self [Self-Schema Processing Task (SSPT)] and ED-specific cognitions about eating, weight and shape (emotional Stroop). The 26-item Eating Attitudes Test (EAT-26; Garner et al. 1982) was used to assess subclinical ED symptoms; this was used as an index of vulnerability within this at-risk group. Regression analyses showed that biases in the processing of both neutral and angry faces were predictive of our measure of vulnerability (EAT-26). In the self-schema task, biases in the processing of negative self descriptors previously found to be common in EDs predicted vulnerability. Biases in the processing of shape-related words on the Stroop task were also predictive; however, these biases were more important in dieters who also displayed biases in the self-schema task. We were also able to demonstrate that these biases are specific and separable from more general negative biases that could be attributed to depressive symptoms. These results suggest that specific biases in the processing of social cues, cognitions about the self, and also about eating, weight and shape information, may be important in understanding risk and preventing relapse in EDs.

  9. Reflecting Equity and Diversity. Part I: Guidelines and Procedure for Evaluating Bias in Instructional Materials. Part II: Bias Awareness Training Worksheets. Part III: Bias Awareness and Procedure Training Course.

    Science.gov (United States)

    Bebermeyer, Jim; Edmond, Mary, Ed.

    Reflecting a need to prepare students for working in diverse organizations, this document was developed to increase school officials' awareness of bias in instructional materials and help them select bias-free materials. A number of the examples illustrate situations dealing with diversity in the workplace. The guide is divided into three parts:…

  10. The self-attribution bias and paranormal beliefs.

    Science.gov (United States)

    van Elk, Michiel

    2017-03-01

    The present study investigated the relation between paranormal beliefs, illusory control and the self-attribution bias, i.e., the motivated tendency to attribute positive outcomes to oneself while negative outcomes are externalized. Visitors of a psychic fair played a card guessing game and indicated their perceived control over randomly selected cards as a function of the congruency and valence of the card. A stronger self-attribution bias was observed for paranormal believers compared to skeptics and this bias was specifically related to traditional religious beliefs and belief in superstition. No relation between paranormal beliefs and illusory control was found. Self-report measures indicated that paranormal beliefs were associated to being raised in a spiritual family and to anomalous experiences during childhood. Thereby this study suggests that paranormal beliefs are related to specific cognitive biases that in turn are shaped by socio-cultural factors. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Exchange bias studied with polarized neutron reflectivity

    International Nuclear Information System (INIS)

    Velthuis, S. G. E. te

    2000-01-01

    The role of Polarized Neutron Reflectivity (PNR) for studying natural and synthetic exchange biased systems is illustrated. For a partially oxidized thin film of Co, cycling of the magnetic field causes a considerable reduction of the bias, which the onset of diffuse neutron scattering shows to be due to the loosening of the ferromagnetic domains. On the other hand, PNR measurements of a model exchange bias junction consisting of an n-layered Fe/Cr antiferromagnetic (AF) superlattice coupled with an m-layered Fe/Cr ferromagnetic (F) superlattice confirm the predicted collinear magnetization in the two superlattices. The two magnetized states of the F (along or opposite to the bias field) differ only in the relative orientation of the F and adjacent AF layer. The possibility of reading clearly the magnetic state at the interface pinpoints the commanding role that PNR is having in solving this intriguing problem

  12. The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland.

    Science.gov (United States)

    Latkin, Carl A; Edwards, Catie; Davey-Rothwell, Melissa A; Tobin, Karin E

    2017-10-01

    Social desirability response bias may lead to inaccurate self-reports and erroneous study conclusions. The present study examined the relationship between social desirability response bias and self-reports of mental health, substance use, and social network factors among a community sample of inner-city substance users. The study was conducted in a sample of 591 opiate and cocaine users in Baltimore, Maryland from 2009 to 2013. Modified items from the Marlowe-Crowne Social Desirability Scale were included in the survey, which was conducted face-to-face and using Audio Computer Self Administering Interview (ACASI) methods. There were highly statistically significant differences in levels of social desirability response bias by levels of depressive symptoms, drug use stigma, physical health status, recent opiate and cocaine use, Alcohol Use Disorders Identification Test (AUDIT) scores, and size of social networks. There were no associations between health service utilization measures and social desirability bias. In multiple logistic regression models, even after including the Center for Epidemiologic Studies Depression Scale (CES-D) as a measure of depressive symptomology, social desirability bias was associated with recent drug use and drug user stigma. Social desirability bias was not associated with enrollment in prior research studies. These findings suggest that social desirability bias is associated with key health measures and that the associations are not primarily due to depressive symptoms. Methods are needed to reduce social desirability bias. Such methods may include the wording and prefacing of questions, clearly defining the role of "study participant," and assessing and addressing motivations for socially desirable responses. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. The source of the truth bias: Heuristic processing?

    Science.gov (United States)

    Street, Chris N H; Masip, Jaume

    2015-06-01

    People believe others are telling the truth more often than they actually are; this is called the truth bias. Surprisingly, when a speaker is judged at multiple points across their statement the truth bias declines. Previous claims argue this is evidence of a shift from (biased) heuristic processing to (reasoned) analytical processing. In four experiments we contrast the heuristic-analytic model (HAM) with alternative accounts. In Experiment 1, the decrease in truth responding was not the result of speakers appearing more deceptive, but was instead attributable to the rater's processing style. Yet contrary to HAMs, across three experiments we found the decline in bias was not related to the amount of processing time available (Experiments 1-3) or the communication channel (Experiment 2). In Experiment 4 we found support for a new account: that the bias reflects whether raters perceive the statement to be internally consistent. © 2015 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  14. A method for additive bias correction in cross-cultural surveys

    DEFF Research Database (Denmark)

    Scholderer, Joachim; Grunert, Klaus G.; Brunsø, Karen

    2001-01-01

    additive bias from cross-cultural data. The procedure involves four steps: (1) embed a potentially biased item in a factor-analytic measurement model, (2) test for the existence of additive bias between populations, (3) use the factor-analytic model to estimate the magnitude of the bias, and (4) replace......Measurement bias in cross-cultural surveys can seriously threaten the validity of hypothesis tests. Direct comparisons of means depend on the assumption that differences in observed variables reflect differences in the underlying constructs, and not an additive bias that may be caused by cultural...... differences in the understanding of item wording or response category labels. However, experience suggests that additive bias can be found more often than not. Based on the concept of partial measurement invariance (Byrne, Shavelson and Muthén, 1989), the present paper develops a procedure for eliminating...

  15. On the Borders of Harmful and Helpful Beauty Biases

    Directory of Open Access Journals (Sweden)

    Maria Agthe

    2016-06-01

    Full Text Available Research with European Caucasian samples demonstrates that attractiveness-based biases in social evaluation depend on the constellation of the sex of the evaluator and the sex of the target: Whereas people generally show positive biases toward attractive opposite-sex persons, they show less positive or even negative biases toward attractive same-sex persons. By examining these biases both within and between different ethnicities, the current studies provide new evidence for both the generalizability and the specificity of these attractiveness-based social perception biases. Examining within-ethnicity effects, Study 1 is the first to demonstrate that samples from diverse ethnic backgrounds parallel the finding of European Caucasian samples: The advantageous or adverse effects of attractiveness depend on the gender constellation of the evaluator and the evaluated person. Examining between-ethnicity effects, Study 2 found that these attractiveness-based biases emerge almost exclusively toward targets of the evaluator’s own ethnic background; these biases were reduced or eliminated for cross-ethnicity evaluations and interaction intentions. We discuss these findings in light of evolutionary principles and reflect on potential interactions between culture and evolved cognitive mechanisms.

  16. Biased Supervision

    OpenAIRE

    Josse Delfgaauw; Michiel Souverijn

    2014-01-01

    markdownabstract__Abstract__ When verifiable performance measures are imperfect, organizations often resort to subjective performance pay. This may give supervisors the power to direct employees towards tasks that mainly benefit the supervisor rather than the organization. We cast a principal-supervisor-agent model in a multitask setting, where the supervisor has an intrinsic preference towards specific tasks. We show that subjective performance pay based on evaluation by a biased supervisor ...

  17. Cognitive biases and decision making in gambling.

    Science.gov (United States)

    Chóliz, Mariano

    2010-08-01

    Heuristics and cognitive biases can occur in reasoning and decision making. Some of them are very common in gamblers (illusion of control, representativeness, availability, etc.). Structural characteristics and functioning of games of chance favor the appearance of these biases. Two experiments were conducted with nonpathological gamblers. The first experiment was a game of dice with wagers. In the second experiment, the participants played two bingo games. Specific rules of the games favored the appearance of cognitive bias (illusion of control) and heuristics (representativeness and availability) and influence on the bets. Results and implications for gambling are discussed.

  18. Liberal bias and the five-factor model.

    Science.gov (United States)

    Charney, Evan

    2015-01-01

    Duarte et al. draw attention to the "embedding of liberal values and methods" in social psychological research. They note how these biases are often invisible to the researchers themselves. The authors themselves fall prey to these "invisible biases" by utilizing the five-factor model of personality and the trait of openness to experience as one possible explanation for the under-representation of political conservatives in social psychology. I show that the manner in which the trait of openness to experience is conceptualized and measured is a particularly blatant example of the very liberal bias the authors decry.

  19. Biasing vector network analyzers using variable frequency and amplitude signals

    Science.gov (United States)

    Nobles, J. E.; Zagorodnii, V.; Hutchison, A.; Celinski, Z.

    2016-08-01

    We report the development of a test setup designed to provide a variable frequency biasing signal to a vector network analyzer (VNA). The test setup is currently used for the testing of liquid crystal (LC) based devices in the microwave region. The use of an AC bias for LC based devices minimizes the negative effects associated with ionic impurities in the media encountered with DC biasing. The test setup utilizes bias tees on the VNA test station to inject the bias signal. The square wave biasing signal is variable from 0.5 to 36.0 V peak-to-peak (VPP) with a frequency range of DC to 10 kHz. The test setup protects the VNA from transient processes, voltage spikes, and high-frequency leakage. Additionally, the signals to the VNA are fused to ½ amp and clipped to a maximum of 36 VPP based on bias tee limitations. This setup allows us to measure S-parameters as a function of both the voltage and the frequency of the applied bias signal.

  20. Toddlers favor communicatively presented information over statistical reliability in learning about artifacts.

    Directory of Open Access Journals (Sweden)

    Hanna Marno

    Full Text Available Observed associations between events can be validated by statistical information of reliability or by testament of communicative sources. We tested whether toddlers learn from their own observation of efficiency, assessed by statistical information on reliability of interventions, or from communicatively presented demonstration, when these two potential types of evidence of validity of interventions on a novel artifact are contrasted with each other. Eighteen-month-old infants observed two adults, one operating the artifact by a method that was more efficient (2/3 probability of success than that of the other (1/3 probability of success. Compared to the Baseline condition, in which communicative signals were not employed, infants tended to choose the less reliable method to operate the artifact when this method was demonstrated in a communicative manner in the Experimental condition. This finding demonstrates that, in certain circumstances, communicative sanctioning of reliability may override statistical evidence for young learners. Such a bias can serve fast and efficient transmission of knowledge between generations.

  1. Efficient bias correction for magnetic resonance image denoising.

    Science.gov (United States)

    Mukherjee, Partha Sarathi; Qiu, Peihua

    2013-05-30

    Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a magnetic resonance scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called Rician noise model. Under that model, the observed image intensity at a given pixel is a nonlinear function of the true image intensity and of two independent zero-mean random variables with the same normal distribution. Because of such a complicated noise structure in the observed MRI images, denoised images by conventional denoising methods are usually biased, and the bias could reduce image contrast and negatively affect subsequent image analysis. Therefore, it is important to address the bias issue properly. To this end, several bias-correction procedures have been proposed in the literature. In this paper, we study the Rician noise model and the corresponding bias-correction problem systematically and propose a new and more effective bias-correction formula based on the regression analysis and Monte Carlo simulation. Numerical studies show that our proposed method works well in various applications. Copyright © 2012 John Wiley & Sons, Ltd.

  2. Corpus-based Transitivity Biases in Individuals with Aphasia

    Directory of Open Access Journals (Sweden)

    Gayle DeDe

    2015-04-01

    Full Text Available Introduction Spontaneous speech samples in individuals with aphasia (IWA have been analyzed to examine many different psycholinguistic features. The present study focused on how IWA use verbs in spontaneous speech. Some verbs can occur in more than one argument structure, but are biased to occur more frequently in one frame than another. For example, "watch" appears in transitive and intransitive structures, but is usually used transitively. This is known as a transitivity bias. It is unknown whether IWA show the same transitivity biases in production as those reported in previous corpus studies with unimpaired individuals. Studies of sentence comprehension have shown that IWA are sensitive to verb biases (e.g., DeDe, 2013. In addition, IWA have shown an overall preference for transitive structures, which are the most frequent structures in English (Roland, Dick, & Elman, 2007. The present study investigated whether IWA show the same pattern of transitive and intransitive biases in spontaneous speech as unimpaired individuals. Method Participants: 278 interviews with IWA were taken from AphasiaBank. The IWA represented a range of aphasia types. Participants were omitted if they spoke English as a second language. Materials: 54 verbs were coded. We chose verbs with the goal of representing different bias types (e.g., transitive, intransitive, sentential complement. Of these, data from 11 transitively biased and 11 intransitively biased verbs (matched for frequency of use and number of syllables are presented here. Coding: All productions of the 54 verbs were coded. The coding protocol was based on Gahl, Jurafsky, and Roland (2004. We implemented an additional level of coding to indicate erroneous verb productions, such as ungrammatical structures and verb agreement errors. Results The (intransitivity biases for IWA were compared to biases from a previously published corpus study (Gahl et al., 2004. The IWA used transitively biased verbs in

  3. A Principal Components Analysis of Dynamic Spatial Memory Biases

    Science.gov (United States)

    Motes, Michael A.; Hubbard, Timothy L.; Courtney, Jon R.; Rypma, Bart

    2008-01-01

    Research has shown that spatial memory for moving targets is often biased in the direction of implied momentum and implied gravity, suggesting that representations of the subjective experiences of these physical principles contribute to such biases. The present study examined the association between these spatial memory biases. Observers viewed…

  4. Dissemination bias in systematic reviews of animal research: a systematic review.

    Directory of Open Access Journals (Sweden)

    Katharina F Mueller

    Full Text Available Systematic reviews of preclinical studies, in vivo animal experiments in particular, can influence clinical research and thus even clinical care. Dissemination bias, selective dissemination of positive or significant results, is one of the major threats to validity in systematic reviews also in the realm of animal studies. We conducted a systematic review to determine the number of published systematic reviews of animal studies until present, to investigate their methodological features especially with respect to assessment of dissemination bias, and to investigate the citation of preclinical systematic reviews on clinical research.Eligible studies for this systematic review constitute systematic reviews that summarize in vivo animal experiments whose results could be interpreted as applicable to clinical care. We systematically searched Ovid Medline, Embase, ToxNet, and ScienceDirect from 1st January 2009 to 9th January 2013 for eligible systematic reviews without language restrictions. Furthermore we included articles from two previous systematic reviews by Peters et al. and Korevaar et al.The literature search and screening process resulted in 512 included full text articles. We found an increasing number of published preclinical systematic reviews over time. The methodological quality of preclinical systematic reviews was low. The majority of preclinical systematic reviews did not assess methodological quality of the included studies (71%, nor did they assess heterogeneity (81% or dissemination bias (87%. Statistics quantifying the importance of clinical research citing systematic reviews of animal studies showed that clinical studies referred to the preclinical research mainly to justify their study or a future study (76%.Preclinical systematic reviews may have an influence on clinical research but their methodological quality frequently remains low. Therefore, systematic reviews of animal research should be critically appraised before

  5. Atlas-based analysis of cardiac shape and function: correction of regional shape bias due to imaging protocol for population studies.

    Science.gov (United States)

    Medrano-Gracia, Pau; Cowan, Brett R; Bluemke, David A; Finn, J Paul; Kadish, Alan H; Lee, Daniel C; Lima, Joao A C; Suinesiaputra, Avan; Young, Alistair A

    2013-09-13

    Cardiovascular imaging studies generate a wealth of data which is typically used only for individual study endpoints. By pooling data from multiple sources, quantitative comparisons can be made of regional wall motion abnormalities between different cohorts, enabling reuse of valuable data. Atlas-based analysis provides precise quantification of shape and motion differences between disease groups and normal subjects. However, subtle shape differences may arise due to differences in imaging protocol between studies. A mathematical model describing regional wall motion and shape was used to establish a coordinate system registered to the cardiac anatomy. The atlas was applied to data contributed to the Cardiac Atlas Project from two independent studies which used different imaging protocols: steady state free precession (SSFP) and gradient recalled echo (GRE) cardiovascular magnetic resonance (CMR). Shape bias due to imaging protocol was corrected using an atlas-based transformation which was generated from a set of 46 volunteers who were imaged with both protocols. Shape bias between GRE and SSFP was regionally variable, and was effectively removed using the atlas-based transformation. Global mass and volume bias was also corrected by this method. Regional shape differences between cohorts were more statistically significant after removing regional artifacts due to imaging protocol bias. Bias arising from imaging protocol can be both global and regional in nature, and is effectively corrected using an atlas-based transformation, enabling direct comparison of regional wall motion abnormalities between cohorts acquired in separate studies.

  6. A pupillary index of susceptibility to decision biases

    OpenAIRE

    Eldar, Eran; Felso, Valkyrie; Cohen, Jonathan; Niv, Yael

    2018-01-01

    Under what conditions do humans systematically deviate from rational decision making? Here we show that pupillary indices of low neural gain are associated with strong and consistent biases across six different extensively-studied decision making tasks, whereas indices of high gain are associated with weak or absent biases. Lower susceptibility to biases, however, comes at the cost of indecisiveness, or alternatively, prolonged deliberation time. We explain the association between low gain an...

  7. Assessing Projection Bias in Consumers' Food Preferences.

    Directory of Open Access Journals (Sweden)

    Tiziana de-Magistris

    Full Text Available The aim of this study is to test whether projection bias exists in consumers' purchasing decisions for food products. To achieve our aim, we used a non-hypothetical experiment (i.e., experimental auction, where hungry and non-hungry participants were incentivized to reveal their willingness to pay (WTP. The results confirm the existence of projection bias when consumers made their decisions on food products. In particular, projection bias existed because currently hungry participants were willing to pay a higher price premium for cheeses than satiated ones, both in hungry and satiated future states. Moreover, participants overvalued the food product more when they were delivered in the future hungry condition than in the satiated one. Our study provides clear, quantitative and meaningful evidence of projection bias because our findings are based on economic valuation of food preferences. Indeed, the strength of this study is that findings are expressed in terms of willingness to pay which is an interpretable amount of money.

  8. Biased Monte Carlo optimization: the basic approach

    International Nuclear Information System (INIS)

    Campioni, Luca; Scardovelli, Ruben; Vestrucci, Paolo

    2005-01-01

    It is well-known that the Monte Carlo method is very successful in tackling several kinds of system simulations. It often happens that one has to deal with rare events, and the use of a variance reduction technique is almost mandatory, in order to have Monte Carlo efficient applications. The main issue associated with variance reduction techniques is related to the choice of the value of the biasing parameter. Actually, this task is typically left to the experience of the Monte Carlo user, who has to make many attempts before achieving an advantageous biasing. A valuable result is provided: a methodology and a practical rule addressed to establish an a priori guidance for the choice of the optimal value of the biasing parameter. This result, which has been obtained for a single component system, has the notable property of being valid for any multicomponent system. In particular, in this paper, the exponential and the uniform biases of exponentially distributed phenomena are investigated thoroughly

  9. "Are cognitive interventions effective in Alzheimer's disease? A controlled meta- analysis of the effects of bias": Correction to Oltra-Cucarella et al. (2016).

    Science.gov (United States)

    2016-07-01

    Reports an error in "Are Cognitive Interventions Effective in Alzheimer's Disease? A Controlled Meta-Analysis of the Effects of Bias" by Javier Oltra-Cucarella, Rubén Pérez-Elvira, Raul Espert and Anita Sohn McCormick (Neuropsychology, Advanced Online Publication, Apr 7, 2016, np). In the article the first sentence of the third paragraph of the Source of bias subsection in the Statistical Analysis subsection of the Correlational Meta-Analysis section should read "For the control condition bias, three comparison groups were differentiated: (a) a structured cognitive intervention, (b) a placebo control condition, and (c) a pharma control condition without cognitive intervention or no treatment at all." (The following abstract of the original article appeared in record 2016-16656-001.) There is limited evidence about the efficacy of cognitive interventions for Alzheimer's disease (AD). However, aside from the methodological quality of the studies analyzed, the methodology used in previous meta-analyses is itself a risk of bias as different types of effect sizes (ESs) were calculated and combined. This study aimed at examining the results of nonpharmacological interventions for AD with an adequate control of statistical methods and to demonstrate a different approach to meta-analysis. ESs were calculated with the independent groups pre/post design. Average ESs for separate outcomes were calculated and moderator analyses were performed so as to offer an overview of the effects of bias. Eighty-seven outcomes from 19 studies (n = 812) were meta-analyzed. ESs were small on average for cognitive and functional outcomes after intervention. Moderator analyses showed no effect of control of bias, although ESs were different from zero only in some circumstances (e.g., memory outcomes in randomized studies). Cognitive interventions showed no more efficacy than placebo interventions, and functional ESs were consistently low across conditions. cognitive interventions delivered

  10. A machine learning model with human cognitive biases capable of learning from small and biased datasets.

    Science.gov (United States)

    Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

    2018-05-09

    Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

  11. A father effect explains sex-ratio bias.

    Science.gov (United States)

    Malo, Aurelio F; Martinez-Pastor, Felipe; Garcia-Gonzalez, Francisco; Garde, Julián; Ballou, Jonathan D; Lacy, Robert C

    2017-08-30

    Sex ratio allocation has important fitness consequences, and theory predicts that parents should adjust offspring sex ratio in cases where the fitness returns of producing male and female offspring vary. The ability of fathers to bias offspring sex ratios has traditionally been dismissed given the expectation of an equal proportion of X- and Y-chromosome-bearing sperm (CBS) in ejaculates due to segregation of sex chromosomes at meiosis. This expectation has been recently refuted. Here we used Peromyscus leucopus to demonstrate that sex ratio is explained by an exclusive effect of the father, and suggest a likely mechanism by which male-driven sex-ratio bias is attained. We identified a male sperm morphological marker that is associated with the mechanism leading to sex ratio bias; differences among males in the sperm nucleus area (a proxy for the sex chromosome that the sperm contains) explain 22% variation in litter sex ratio. We further show the role played by the sperm nucleus area as a mediator in the relationship between individual genetic variation and sex-ratio bias. Fathers with high levels of genetic variation had ejaculates with a higher proportion of sperm with small nuclei area. This, in turn, led to siring a higher proportion of sons (25% increase in sons per 0.1 decrease in the inbreeding coefficient). Our results reveal a plausible mechanism underlying unexplored male-driven sex-ratio biases. We also discuss why this pattern of paternal bias can be adaptive. This research puts to rest the idea that father contribution to sex ratio variation should be disregarded in vertebrates, and will stimulate research on evolutionary constraints to sex ratios-for example, whether fathers and mothers have divergent, coinciding, or neutral sex allocation interests. Finally, these results offer a potential explanation for those intriguing cases in which there are sex ratio biases, such as in humans. © 2017 The Author(s).

  12. A New Source Biasing Approach in ADVANTG

    International Nuclear Information System (INIS)

    Bevill, Aaron M.; Mosher, Scott W.

    2012-01-01

    The ADVANTG code has been developed at Oak Ridge National Laboratory to generate biased sources and weight window maps for MCNP using the CADIS and FW-CADIS methods. In preparation for an upcoming RSICC release, a new approach for generating a biased source has been developed. This improvement streamlines user input and improves reliability. Previous versions of ADVANTG generated the biased source from ADVANTG input, writing an entirely new general fixed-source definition (SDEF). Because volumetric sources were translated into SDEF-format as a finite set of points, the user had to perform a convergence study to determine whether the number of source points used accurately represented the source region. Further, the large number of points that must be written in SDEF-format made the MCNP input and output files excessively long and difficult to debug. ADVANTG now reads SDEF-format distributions and generates corresponding source biasing cards, eliminating the need for a convergence study. Many problems of interest use complicated source regions that are defined using cell rejection. In cell rejection, the source distribution in space is defined using an arbitrarily complex cell and a simple bounding region. Source positions are sampled within the bounding region but accepted only if they fall within the cell; otherwise, the position is resampled entirely. When biasing in space is applied to sources that use rejection sampling, current versions of MCNP do not account for the rejection in setting the source weight of histories, resulting in an 'unfair game'. This problem was circumvented in previous versions of ADVANTG by translating volumetric sources into a finite set of points, which does not alter the mean history weight ((bar w)). To use biasing parameters without otherwise modifying the original cell-rejection SDEF-format source, ADVANTG users now apply a correction factor for (bar w) in post-processing. A stratified-random sampling approach in ADVANTG is under

  13. Significant biases affecting abundance determinations

    Science.gov (United States)

    Wesson, Roger

    2015-08-01

    I have developed two highly efficient codes to automate analyses of emission line nebulae. The tools place particular emphasis on the propagation of uncertainties. The first tool, ALFA, uses a genetic algorithm to rapidly optimise the parameters of gaussian fits to line profiles. It can fit emission line spectra of arbitrary resolution, wavelength range and depth, with no user input at all. It is well suited to highly multiplexed spectroscopy such as that now being carried out with instruments such as MUSE at the VLT. The second tool, NEAT, carries out a full analysis of emission line fluxes, robustly propagating uncertainties using a Monte Carlo technique.Using these tools, I have found that considerable biases can be introduced into abundance determinations if the uncertainty distribution of emission lines is not well characterised. For weak lines, normally distributed uncertainties are generally assumed, though it is incorrect to do so, and significant biases can result. I discuss observational evidence of these biases. The two new codes contain routines to correctly characterise the probability distributions, giving more reliable results in analyses of emission line nebulae.

  14. The Local Bias of Individual Investors

    OpenAIRE

    Ning Zhu

    2002-01-01

    This study investigates individual investors' bias towards nearby companies. Using data from a large U.S. discount brokerage, we find that individual investors tend to invest in companies closer to them relative to the market portfolio. Unlike Coval and Moskowitz's (1999) findings on institutional investors, however, we find that advantageous information cannot explain individual investors' local bias. Accounting numbers and information asymmetry matter less to individual investors' local bia...

  15. Reporting bias in medical research - a narrative review

    Directory of Open Access Journals (Sweden)

    Kölsch Heike

    2010-04-01

    Full Text Available Abstract Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles. We identified reporting bias in 40 indications comprising around 50 different pharmacological, surgical (e.g. vacuum-assisted closure therapy, diagnostic (e.g. ultrasound, and preventive (e.g. cancer vaccines interventions. Regarding pharmacological interventions, cases of reporting bias were, for example, identified in the treatment of the following conditions: depression, bipolar disorder, schizophrenia, anxiety disorder, attention-deficit hyperactivity disorder, Alzheimer's disease, pain, migraine, cardiovascular disease, gastric ulcers, irritable bowel syndrome, urinary incontinence, atopic dermatitis, diabetes mellitus type 2, hypercholesterolaemia, thyroid disorders, menopausal symptoms, various types of cancer (e.g. ovarian cancer and melanoma, various types of infections (e.g. HIV, influenza and Hepatitis B, and acute trauma. Many cases involved the withholding of study data by manufacturers and regulatory agencies or the active attempt by manufacturers to suppress publication. The ascertained effects of reporting bias included the

  16. Referral bias in hospital register studies of geographical and industrial differences in health.

    Science.gov (United States)

    Soll-Johanning, Helle; Hannerz, Harald; Tüchsen, Finn

    2004-05-01

    The Danish National Hospital Register contains four patient types: full-time inpatients, part-time inpatients, outpatients and emergency ward patients. The aim of the present study was to investigate whether results from comparative hospital register studies depend on which patient types we choose to include in the analysis. The hospital register was linked to the centralised civil register and the employment classification module. All economically active persons in Denmark aged 20-59 years 1st January 1995 (N = 2,281,480) were followed for six years. We calculated SIRs, first by county then by industry and finally by industry adjusted for county, for a variety of diagnostic groups and for each of the following types of cases: A) full-time inpatients, B) all inpatients, C) all inpatients and outpatients, D) all patients. The ratio between the maximum and the minimum of the four types of SIRs was calculated for each combination of the examined population groups and diseases. A max/min ratio was regarded as a sign of referral bias if it was above 1.2 and statistically significant. When calculating SIRs by county 46.7 percent of the max/min ratios signified referral bias. The percentage was 5.5 when calculating SIRs by industry and only 1.7 when they were calculated by industry adjusted for county. Estimates of geographical health differences are often distorted by differences in the health care organisation. Estimates of industrial health differences tend to be robust with a few identifiable exceptions. Standardisation for county will eliminate bias.

  17. Revelation of Influencing Factors in Overall Codon Usage Bias of Equine Influenza Viruses.

    Directory of Open Access Journals (Sweden)

    Naveen Kumar

    Full Text Available Equine influenza viruses (EIVs of H3N8 subtype are culprits of severe acute respiratory infections in horses, and are still responsible for significant outbreaks worldwide. Adaptability of influenza viruses to a particular host is significantly influenced by their codon usage preference, due to an absolute dependence on the host cellular machinery for their replication. In the present study, we analyzed genome-wide codon usage patterns in 92 EIV strains, including both H3N8 and H7N7 subtypes by computing several codon usage indices and applying multivariate statistical methods. Relative synonymous codon usage (RSCU analysis disclosed bias of preferred synonymous codons towards A/U-ended codons. The overall codon usage bias in EIVs was slightly lower, and mainly affected by the nucleotide compositional constraints as inferred from the RSCU and effective number of codon (ENc analysis. Our data suggested that codon usage pattern in EIVs is governed by the interplay of mutation pressure, natural selection from its hosts and undefined factors. The H7N7 subtype was found less fit to its host (horse in comparison to H3N8, by possessing higher codon bias, lower mutation pressure and much less adaptation to tRNA pool of equine cells. To the best of our knowledge, this is the first report describing the codon usage analysis of the complete genomes of EIVs. The outcome of our study is likely to enhance our understanding of factors involved in viral adaptation, evolution, and fitness towards their hosts.

  18. Revelation of Influencing Factors in Overall Codon Usage Bias of Equine Influenza Viruses

    Science.gov (United States)

    Bhatia, Sandeep; Sood, Richa; Selvaraj, Pavulraj

    2016-01-01

    Equine influenza viruses (EIVs) of H3N8 subtype are culprits of severe acute respiratory infections in horses, and are still responsible for significant outbreaks worldwide. Adaptability of influenza viruses to a particular host is significantly influenced by their codon usage preference, due to an absolute dependence on the host cellular machinery for their replication. In the present study, we analyzed genome-wide codon usage patterns in 92 EIV strains, including both H3N8 and H7N7 subtypes by computing several codon usage indices and applying multivariate statistical methods. Relative synonymous codon usage (RSCU) analysis disclosed bias of preferred synonymous codons towards A/U-ended codons. The overall codon usage bias in EIVs was slightly lower, and mainly affected by the nucleotide compositional constraints as inferred from the RSCU and effective number of codon (ENc) analysis. Our data suggested that codon usage pattern in EIVs is governed by the interplay of mutation pressure, natural selection from its hosts and undefined factors. The H7N7 subtype was found less fit to its host (horse) in comparison to H3N8, by possessing higher codon bias, lower mutation pressure and much less adaptation to tRNA pool of equine cells. To the best of our knowledge, this is the first report describing the codon usage analysis of the complete genomes of EIVs. The outcome of our study is likely to enhance our understanding of factors involved in viral adaptation, evolution, and fitness towards their hosts. PMID:27119730

  19. Race, Socioeconomic Status, and Implicit Bias: Implications for Closing the Achievement Gap

    Science.gov (United States)

    Schlosser, Elizabeth Auretta Cox

    This study accessed the relationship between race, socioeconomic status, age and the race implicit bias held by middle and high school science teachers in Mobile and Baldwin County Public School Systems. Seventy-nine participants were administered the race Implicit Association Test (race IAT), created by Greenwald, A. G., Nosek, B. A., & Banaji, M. R., (2003) and a demographic survey. Quantitative analysis using analysis of variances, ANOVA and t-tests were used in this study. An ANOVA was performed comparing the race IAT scores of African American science teachers and their Caucasian counterparts. A statically significant difference was found (F = .4.56, p = .01). An ANOVA was also performed using the race IAT scores comparing the age of the participants; the analysis yielded no statistical difference based on age. A t-test was performed comparing the race IAT scores of African American teachers who taught at either Title I or non-Title I schools; no statistical difference was found between groups (t = -17.985, p .001). This research examines the implications of the achievement gap among African American and Caucasian students in science.

  20. Bias-dependent hybrid PKI empirical-neural model of microwave FETs

    Science.gov (United States)

    Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera

    2011-10-01

    Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.