Biased selection within the social health insurance market in Colombia.

Science.gov (United States)

Castano, Ramon; Zambrano, Andres

2006-12-01

Reducing the impact of insurance market failures with regulations such as community-rated premiums, standardized benefit packages and open enrolment, yield limited effect because they create room for selection bias. The Colombian social health insurance system started a market approach in 1993 expecting to improve performance of preexisting monopolistic insurance funds by exposing them to competition by new entrants. This paper tests the hypothesis that market failures would lead to biased selection favoring new entrants. Two household surveys are analyzed using Self-Reported Health Status and the presence of chronic conditions as prospective indicators of individual risk. Biased selection is found to take place, leading to adverse selection among incumbents, and favorable selection among new entrants. This pattern is absent in 1997 but is evident in 2003. Given that the two incumbents analyzed are public organizations, the fiscal implications of the findings in terms of government bailouts, are analyzed.

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...

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...

Selection bias and the Rubin-Ford effect

International Nuclear Information System (INIS)

James, P.A.; Joseph, R.D.; Collins, C.A.

1991-01-01

We have re-examined the 'Rubin-Ford effect', and more recent claims of galaxy streaming from the same galaxy sample, to investigate the impact of selection effects on these results. A 'Monte Carlo'-type analysis was applied to simulate the selection procedure used to obtain this sample, and a strong bias was identified, resulting in apparent velocity flows at 600-800 km s -1 . Thus the 'Rubin-Ford effect' and the associated galaxy streaming are spurious effects resulting from the method of sample selection. (author)

Entropy-based gene ranking without selection bias for the predictive classification of microarray data

Directory of Open Access Journals (Sweden)

Serafini Maria

2003-11-01

Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

[Inverse probability weighting (IPW) for evaluating and "correcting" selection bias].

Science.gov (United States)

Narduzzi, Silvia; Golini, Martina Nicole; Porta, Daniela; Stafoggia, Massimo; Forastiere, Francesco

2014-01-01

the Inverse probability weighting (IPW) is a methodology developed to account for missingness and selection bias caused by non-randomselection of observations, or non-random lack of some information in a subgroup of the population. to provide an overview of IPW methodology and an application in a cohort study of the association between exposure to traffic air pollution (nitrogen dioxide, NO₂) and 7-year children IQ. this methodology allows to correct the analysis by weighting the observations with the probability of being selected. The IPW is based on the assumption that individual information that can predict the probability of inclusion (non-missingness) are available for the entire study population, so that, after taking account of them, we can make inferences about the entire target population starting from the nonmissing observations alone.The procedure for the calculation is the following: firstly, we consider the entire population at study and calculate the probability of non-missing information using a logistic regression model, where the response is the nonmissingness and the covariates are its possible predictors.The weight of each subject is given by the inverse of the predicted probability. Then the analysis is performed only on the non-missing observations using a weighted model. IPW is a technique that allows to embed the selection process in the analysis of the estimates, but its effectiveness in "correcting" the selection bias depends on the availability of enough information, for the entire population, to predict the non-missingness probability. In the example proposed, the IPW application showed that the effect of exposure to NO2 on the area of verbal intelligence quotient of children is stronger than the effect showed from the analysis performed without regard to the selection processes.

A Heckman Selection- t Model

KAUST Repository

Marchenko, Yulia V.; Genton, Marc G.

2012-01-01

for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical

Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage

DEFF Research Database (Denmark)

Yang, Ziheng; Nielsen, Rasmus

2008-01-01

Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we impl...... codon usage in mammals. Estimates of selection coefficients nevertheless suggest that selection on codon usage is weak and most mutations are nearly neutral. The sensitivity of the analysis on the assumed mutation model is discussed.......Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we...... implement a few population genetics models of codon substitution that explicitly consider mutation bias and natural selection at the DNA level. Selection on codon usage is modeled by introducing codon-fitness parameters, which together with mutation-bias parameters, predict optimal codon frequencies...

Selection bias in dynamically measured supermassive black hole samples: scaling relations and correlations between residuals in semi-analytic galaxy formation models

Science.gov (United States)

Barausse, Enrico; Shankar, Francesco; Bernardi, Mariangela; Dubois, Yohan; Sheth, Ravi K.

2017-07-01

Recent work has confirmed that the scaling relations between the masses of supermassive black holes and host-galaxy properties such as stellar masses and velocity dispersions may be biased high. Much of this may be caused by the requirement that the black hole sphere of influence must be resolved for the black hole mass to be reliably estimated. We revisit this issue with a comprehensive galaxy evolution semi-analytic model. Once tuned to reproduce the (mean) correlation of black hole mass with velocity dispersion, the model cannot account for the correlation with stellar mass. This is independent of the model's parameters, thus suggesting an internal inconsistency in the data. The predicted distributions, especially at the low-mass end, are also much broader than observed. However, if selection effects are included, the model's predictions tend to align with the observations. We also demonstrate that the correlations between the residuals of the scaling relations are more effective than the relations themselves at constraining models for the feedback of active galactic nuclei (AGNs). In fact, we find that our model, while in apparent broad agreement with the scaling relations when accounting for selection biases, yields very weak correlations between their residuals at fixed stellar mass, in stark contrast with observations. This problem persists when changing the AGN feedback strength, and is also present in the hydrodynamic cosmological simulation Horizon-AGN, which includes state-of-the-art treatments of AGN feedback. This suggests that current AGN feedback models are too weak or simply not capturing the effect of the black hole on the stellar velocity dispersion.

Selection bias in the reported performances of AD classification pipelines

Directory of Open Access Journals (Sweden)

Alex F. Mendelson

2017-01-01

Full Text Available The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation.

On a Robust MaxEnt Process Regression Model with Sample-Selection

Directory of Open Access Journals (Sweden)

Hea-Jung Kim

2018-04-01

Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

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.

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

Role Appropriateness of Educational Fields: Bias in Selection.

Science.gov (United States)

Smith, Elizabeth P.; And Others

Bias towards women exists in the selection of applicants to professional and other positions. This research investigated the effects of two rater variables--sex and attitude toward women--and three applicant variables--sex, field (engineering-dietetics), and attributes--(feminine-masculine) upon ratings of competency and personal charm. Analyses…

Associations among Selective Attention, Memory Bias, Cognitive Errors and Symptoms of Anxiety in Youth

Science.gov (United States)

Watts, Sarah E.; Weems, Carl F.

2006-01-01

The purpose of this study was to examine the linkages among selective attention, memory bias, cognitive errors, and anxiety problems by testing a model of the interrelations among these cognitive variables and childhood anxiety disorder symptoms. A community sample of 81 youth (38 females and 43 males) aged 9-17 years and their parents completed…

Age-ordered shirt numbering reduces the selection bias associated with the relative age effect.

Science.gov (United States)

Mann, David L; van Ginneken, Pleun J M A

2017-04-01

When placed into age groups for junior sporting competition, the relative differences in age between children leads to a bias in who is evaluated as being talented. While the impact of this relative age effect (RAE) is clear, until now there has been no evidence to show how to reduce it. The aim of this study was to determine whether the selection bias associated with the RAE could be reduced. Talent scouts from an elite football club watched junior games and ranked players on the basis of their potential. Scouts were allocated to one of three groups provided with contrasting information about the age of the players: (1) no age information, (2) players' birthdates or (3) knowledge that the numbers on the playing shirts corresponded to the relative age of the players. Results revealed a significant selection bias for the scouts in the no-age information group, and that bias remained when scouts knew the players' dates-of-birth. Strikingly though, the selection bias was eliminated when scouts watched the games knowing the shirt numbers corresponded to the relative ages of the players. The selection bias associated with the RAE can be reduced if information about age is presented appropriately.

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.

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

Running Performance, VO2max, and Running Economy: The Widespread Issue of Endogenous Selection Bias.

Science.gov (United States)

Borgen, Nicolai T

2018-05-01

Studies in sport and exercise medicine routinely use samples of highly trained individuals in order to understand what characterizes elite endurance performance, such as running economy and maximal oxygen uptake VO 2max . However, it is not well understood in the literature that using such samples most certainly leads to biased findings and accordingly potentially erroneous conclusions because of endogenous selection bias. In this paper, I review the current literature on running economy and VO 2max , and discuss the literature in light of endogenous selection bias. I demonstrate that the results in a large part of the literature may be misleading, and provide some practical suggestions as to how future studies may alleviate endogenous selection bias.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

Science.gov (United States)

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Reducing selection bias in case-control studies from rare disease registries.

Science.gov (United States)

Cole, J Alexander; Taylor, John S; Hangartner, Thomas N; Weinreb, Neal J; Mistry, Pramod K; Khan, Aneal

2011-09-12

In clinical research of rare diseases, where small patient numbers and disease heterogeneity limit study design options, registries are a valuable resource for demographic and outcome information. However, in contrast to prospective, randomized clinical trials, the observational design of registries is prone to introduce selection bias and negatively impact the validity of data analyses. The objective of the study was to demonstrate the utility of case-control matching and the risk-set method in order to control bias in data from a rare disease registry. Data from the International Collaborative Gaucher Group (ICGG) Gaucher Registry were used as an example. A case-control matching analysis using the risk-set method was conducted to identify two groups of patients with type 1 Gaucher disease in the ICGG Gaucher Registry: patients with avascular osteonecrosis (AVN) and those without AVN. The frequency distributions of gender, decade of birth, treatment status, and splenectomy status were presented for cases and controls before and after matching. Odds ratios (and 95% confidence intervals) were calculated for each variable before and after matching. The application of case-control matching methodology results in cohorts of cases (i.e., patients with AVN) and controls (i.e., patients without AVN) who have comparable distributions for four common parameters used in subject selection: gender, year of birth (age), treatment status, and splenectomy status. Matching resulted in odds ratios of approximately 1.00, indicating no bias. We demonstrated bias in case-control selection in subjects from a prototype rare disease registry and used case-control matching to minimize this bias. Therefore, this approach appears useful to study cohorts of heterogeneous patients in rare disease registries.

Bayesian Model Selection under Time Constraints

Science.gov (United States)

Hoege, M.; Nowak, W.; Illman, W. A.

2017-12-01

Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

On the model-dependence of the relation between minimum-bias and inelastic proton-proton cross sections

International Nuclear Information System (INIS)

Ostapchenko, S.

2011-01-01

The model-dependence of the relation between the inelastic and various minimum-bias proton-proton cross sections is analyzed, paying a special attention to the sensitivity of minimum-bias triggers to diffractive collisions. Concentrating on the trigger selections of the ATLAS experiment, the measured cross sections are compared to predictions of a number of hadronic Monte Carlo models used in the cosmic ray field. It is demonstrated that the ATLAS results are able to discriminate between different models and between certain theoretical approaches for soft multi-particle production. On the other hand, the strong model-dependence of the selection efficiency of the minimum-bias triggers prevents one from inferring high mass diffraction rate from the discussed data. Moreover, the measured cross sections prove to be insensitive to the production of low mass diffractive states in proton-proton collisions. Consequently, a reliable determination of the total inelastic cross section requires forward proton tracking by a dedicated experiment.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection

OpenAIRE

Kwan, Johnny S. H.; Kung, Annie W. C.; Sham, Pak C.

2011-01-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias. © The Author(s) 2011.

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.

Representation Bias, Return Forecast, and Portfolio Selection in the Stock Market of China

Directory of Open Access Journals (Sweden)

Daping Zhao

2014-01-01

Full Text Available Representation bias means a kind of cognitive tendency, and, for investors, it can affect their behavior in the stock market. Whether the representation bias can help the return forecast and portfolio selection is an interesting problem that is less studied. In this paper, based on the representation bias theory and current markets situation in China, a new hierarchy of stock measurement system is constructed and a corresponding set of criteria is also proposed. On each criterion, we try to measure the influence among stocks with adapted fuzzy AHP. Then the Hausdorff distance is applied to weight and compute the horizontal representation returns. For the forecast returns, according to representation behaviors, there is also a new computation method. Empirical results show that the representation bias information is useful to the return forecast as well as the portfolio selection.

Reducing selection bias in case-control studies from rare disease registries

Directory of Open Access Journals (Sweden)

Mistry Pramod K

2011-09-01

Full Text Available Abstract Background In clinical research of rare diseases, where small patient numbers and disease heterogeneity limit study design options, registries are a valuable resource for demographic and outcome information. However, in contrast to prospective, randomized clinical trials, the observational design of registries is prone to introduce selection bias and negatively impact the validity of data analyses. The objective of the study was to demonstrate the utility of case-control matching and the risk-set method in order to control bias in data from a rare disease registry. Data from the International Collaborative Gaucher Group (ICGG Gaucher Registry were used as an example. Methods A case-control matching analysis using the risk-set method was conducted to identify two groups of patients with type 1 Gaucher disease in the ICGG Gaucher Registry: patients with avascular osteonecrosis (AVN and those without AVN. The frequency distributions of gender, decade of birth, treatment status, and splenectomy status were presented for cases and controls before and after matching. Odds ratios (and 95% confidence intervals were calculated for each variable before and after matching. Results The application of case-control matching methodology results in cohorts of cases (i.e., patients with AVN and controls (i.e., patients without AVN who have comparable distributions for four common parameters used in subject selection: gender, year of birth (age, treatment status, and splenectomy status. Matching resulted in odds ratios of approximately 1.00, indicating no bias. Conclusions We demonstrated bias in case-control selection in subjects from a prototype rare disease registry and used case-control matching to minimize this bias. Therefore, this approach appears useful to study cohorts of heterogeneous patients in rare disease registries.

Bias correction for selecting the minimal-error classifier from many machine learning models.

Science.gov (United States)

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias

International Nuclear Information System (INIS)

Yoo, Seung-Hoon; Lim, Hea-Jin; Kwak, Seung-Jun

2009-01-01

Over the last twenty years, the consumption of natural gas in Korea has increased dramatically. This increase has mainly resulted from the rise of consumption in the residential sector. The main objective of the study is to estimate households' demand function for natural gas by applying a sample selection model using data from a survey of households in Seoul. The results show that there exists a selection bias in the sample and that failure to correct for sample selection bias distorts the mean estimate, of the demand for natural gas, downward by 48.1%. In addition, according to the estimation results, the size of the house, the dummy variable for dwelling in an apartment, the dummy variable for having a bed in an inner room, and the household's income all have positive relationships with the demand for natural gas. On the other hand, the size of the family and the price of gas negatively contribute to the demand for natural gas. (author)

Selection bias in population-based cancer case-control studies due to incomplete sampling frame coverage.

Science.gov (United States)

Walsh, Matthew C; Trentham-Dietz, Amy; Gangnon, Ronald E; Nieto, F Javier; Newcomb, Polly A; Palta, Mari

2012-06-01

Increasing numbers of individuals are choosing to opt out of population-based sampling frames due to privacy concerns. This is especially a problem in the selection of controls for case-control studies, as the cases often arise from relatively complete population-based registries, whereas control selection requires a sampling frame. If opt out is also related to risk factors, bias can arise. We linked breast cancer cases who reported having a valid driver's license from the 2004-2008 Wisconsin women's health study (N = 2,988) with a master list of licensed drivers from the Wisconsin Department of Transportation (WDOT). This master list excludes Wisconsin drivers that requested their information not be sold by the state. Multivariate-adjusted selection probability ratios (SPR) were calculated to estimate potential bias when using this driver's license sampling frame to select controls. A total of 962 cases (32%) had opted out of the WDOT sampling frame. Cases age <40 (SPR = 0.90), income either unreported (SPR = 0.89) or greater than $50,000 (SPR = 0.94), lower parity (SPR = 0.96 per one-child decrease), and hormone use (SPR = 0.93) were significantly less likely to be covered by the WDOT sampling frame (α = 0.05 level). Our results indicate the potential for selection bias due to differential opt out between various demographic and behavioral subgroups of controls. As selection bias may differ by exposure and study base, the assessment of potential bias needs to be ongoing. SPRs can be used to predict the direction of bias when cases and controls stem from different sampling frames in population-based case-control studies.

Mixed signals: The effect of conflicting reward- and goal-driven biases on selective attention

OpenAIRE

Preciado, Daniel; Munneke, Jaap; Theeuwes, Jan

2017-01-01

Attentional selection depends on the interaction between exogenous (stimulus-driven), endogenous (goal-driven), and selection history (experience-driven) factors. While endogenous and exogenous biases have been widely investigated, less is known about their interplay with value-driven attention. The present study investigated the interaction between reward-history and goal-driven biases on perceptual sensitivity (d?) and response time (RT) in a modified cueing paradigm presenting two coloured...

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.

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

2012-01-10

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Science.gov (United States)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Evaluation of bias associated with capture maps derived from nonlinear groundwater flow models

Science.gov (United States)

Nadler, Cara; Allander, Kip K.; Pohll, Greg; Morway, Eric D.; Naranjo, Ramon C.; Huntington, Justin

2018-01-01

The impact of groundwater withdrawal on surface water is a concern of water users and water managers, particularly in the arid western United States. Capture maps are useful tools to spatially assess the impact of groundwater pumping on water sources (e.g., streamflow depletion) and are being used more frequently for conjunctive management of surface water and groundwater. Capture maps have been derived using linear groundwater flow models and rely on the principle of superposition to demonstrate the effects of pumping in various locations on resources of interest. However, nonlinear models are often necessary to simulate head-dependent boundary conditions and unconfined aquifers. Capture maps developed using nonlinear models with the principle of superposition may over- or underestimate capture magnitude and spatial extent. This paper presents new methods for generating capture difference maps, which assess spatial effects of model nonlinearity on capture fraction sensitivity to pumping rate, and for calculating the bias associated with capture maps. The sensitivity of capture map bias to selected parameters related to model design and conceptualization for the arid western United States is explored. This study finds that the simulation of stream continuity, pumping rates, stream incision, well proximity to capture sources, aquifer hydraulic conductivity, and groundwater evapotranspiration extinction depth substantially affect capture map bias. Capture difference maps demonstrate that regions with large capture fraction differences are indicative of greater potential capture map bias. Understanding both spatial and temporal bias in capture maps derived from nonlinear groundwater flow models improves their utility and defensibility as conjunctive-use management tools.

Gender Wage Gap Accounting: The Role of Selection Bias.

Science.gov (United States)

Bar, Michael; Kim, Seik; Leukhina, Oksana

2015-10-01

Mulligan and Rubinstein (2008) (MR) argued that changing selection of working females on unobservable characteristics, from negative in the 1970s to positive in the 1990s, accounted for nearly the entire closing of the gender wage gap. We argue that their female wage equation estimates are inconsistent. Correcting this error substantially weakens the role of the rising selection bias (39 % versus 78 %) and strengthens the contribution of declining discrimination (42 % versus 7 %). Our findings resonate better with related literature. We also explain why our finding of positive selection in the 1970s provides additional support for MR's main hypothesis that an exogenous rise in the market value of unobservable characteristics contributed to the closing of the gender gap.

Alcohol-related biases in selective attention and action tendency make distinct contributions to dysregulated drinking behaviour.

Science.gov (United States)

Sharbanee, Jason M; Stritzke, Werner G K; Wiers, Reinout W; MacLeod, Colin

2013-10-01

To assess whether alcohol-related biases in selective-attention and action tendency uniquely or concurrently predict the ability to regulate alcohol consumption. Two groups of undergraduate social drinkers (total n = 55) who differed in their ability to regulate their alcohol consumption completed a novel Selective-Attention/Action-Tendency Task (SA/ATT), which assessed separately alcohol-related biases in selective attention and action tendency. University of Western Australia, Australia. Dysregulated drinking was operationalized as a self-reported high level of alcohol consumption on the Alcohol Consumption Questionnaire, and a high desire to reduce consumption on the Brief Readiness to Change Algorithm. Selective attention and action tendency were assessed using the SA/ATT, working memory was assessed using the operation-span task and participant characteristics were assessed using the Alcohol Use Disorders Identification Test (AUDIT) and Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Results indicated that (i) there was no significant association between alcohol-related biases in selective attention and action tendency, r = 0.16, P = 0.274, and (ii) biases towards alcohol, in both selective attention, β = 1.01, odds ratio = 2.74, P = 0.022, and action tendency, β = 1.24, odds ratio = 3.45, P = 0.015, predicted independent variance in dysregulated-drinker status. Biases in selective attention and action tendency appear to be distinct mechanisms that contribute independently to difficulty regulating alcohol consumption. Treatment components that could be combined to target both mechanisms could enhance treatment outcomes for alcohol-use disorders. © 2013 Society for the Study of Addiction.

Racial Bias and Predictive Validity in Testing for Selection.

Science.gov (United States)

1983-07-01

the inequa - lity rR (P.C) *0 (2) must define test bias. This definition of test bias conforms to the requirements of the Civil Rights Act of 1964 as...of Educational Measurement, 1976, 13, 43-52. Einhorn, H. J., & Bass, A. R. Methodological considerations relevant to discrimination in employment ...34unbiased" selec- tion model: A question of utilities. Journal of Applied Psychology, 1975, 60, 345-351. Guion, R. M. Employment tests and discriminatory

A model-based correction for outcome reporting bias in meta-analysis.

Science.gov (United States)

Copas, John; Dwan, Kerry; Kirkham, Jamie; Williamson, Paula

2014-04-01

It is often suspected (or known) that outcomes published in medical trials are selectively reported. A systematic review for a particular outcome of interest can only include studies where that outcome was reported and so may omit, for example, a study that has considered several outcome measures but only reports those giving significant results. Using the methodology of the Outcome Reporting Bias (ORB) in Trials study of (Kirkham and others, 2010. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. British Medical Journal 340, c365), we suggest a likelihood-based model for estimating the effect of ORB on confidence intervals and p-values in meta-analysis. Correcting for bias has the effect of moving estimated treatment effects toward the null and hence more cautious assessments of significance. The bias can be very substantial, sometimes sufficient to completely overturn previous claims of significance. We re-analyze two contrasting examples, and derive a simple fixed effects approximation that can be used to give an initial estimate of the effect of ORB in practice.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

Science.gov (United States)

Syfert, Mindy M; Smith, Matthew J; Coomes, David A

2013-01-01

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Sniffing out the Secret Poison: Selection Bias in Educational Research

Science.gov (United States)

Showalter, Daniel A.; Mullet, Luke B.

2017-01-01

Selection bias is a persistent, and often hidden, problem in educational research. It is the primary obstacle standing in between increasingly available large education datasets and the ability to make valid causal inferences to inform policymaking, research, and practice (Stuart, 2010). This article provides an accessible discussion on the…

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.

Functional conservation of nucleosome formation selectively biases presumably neutral molecular variation in yeast genomes.

Science.gov (United States)

Babbitt, Gregory A; Cotter, C R

2011-01-01

One prominent pattern of mutational frequency, long appreciated in comparative genomics, is the bias of purine/pyrimidine conserving substitutions (transitions) over purine/pyrimidine altering substitutions (transversions). Traditionally, this transitional bias has been thought to be driven by the underlying rates of DNA mutation and/or repair. However, recent sequencing studies of mutation accumulation lines in model organisms demonstrate that substitutions generally do not accumulate at rates that would indicate a transitional bias. These observations have called into question a very basic assumption of molecular evolution; that naturally occurring patterns of molecular variation in noncoding regions accurately reflect the underlying processes of randomly accumulating neutral mutation in nuclear genomes. Here, in Saccharomyces yeasts, we report a very strong inverse association (r = -0.951, P < 0.004) between the genome-wide frequency of substitutions and their average energetic effect on nucleosome formation, as predicted by a structurally based energy model of DNA deformation around the nucleosome core. We find that transitions occurring at sites positioned nearest the nucleosome surface, which are believed to function most importantly in nucleosome formation, alter the deformation energy of DNA to the nucleosome core by only a fraction of the energy changes typical of most transversions. When we examined the same substitutions set against random background sequences as well as an existing study reporting substitutions arising in mutation accumulation lines of Saccharomyces cerevisiae, we failed to find a similar relationship. These results support the idea that natural selection acting to functionally conserve chromatin organization may contribute significantly to genome-wide transitional bias, even in noncoding regions. Because nucleosome core structure is highly conserved across eukaryotes, our observations may also help to further explain locally elevated

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)

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.

The Accuracy and Bias of Single-Step Genomic Prediction for Populations Under Selection

Directory of Open Access Journals (Sweden)

Wan-Ling Hsu

2017-08-01

Full Text Available In single-step analyses, missing genotypes are explicitly or implicitly imputed, and this requires centering the observed genotypes using the means of the unselected founders. If genotypes are only available for selected individuals, centering on the unselected founder mean is not straightforward. Here, computer simulation is used to study an alternative analysis that does not require centering genotypes but fits the mean μg of unselected individuals as a fixed effect. Starting with observed diplotypes from 721 cattle, a five-generation population was simulated with sire selection to produce 40,000 individuals with phenotypes, of which the 1000 sires had genotypes. The next generation of 8000 genotyped individuals was used for validation. Evaluations were undertaken with (J or without (N μg when marker covariates were not centered; and with (JC or without (C μg when all observed and imputed marker covariates were centered. Centering did not influence accuracy of genomic prediction, but fitting μg did. Accuracies were improved when the panel comprised only quantitative trait loci (QTL; models JC and J had accuracies of 99.4%, whereas models C and N had accuracies of 90.2%. When only markers were in the panel, the 4 models had accuracies of 80.4%. In panels that included QTL, fitting μg in the model improved accuracy, but had little impact when the panel contained only markers. In populations undergoing selection, fitting μg in the model is recommended to avoid bias and reduction in prediction accuracy due to selection.

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.

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.

The Impact of Selection, Gene Conversion, and Biased Sampling on the Assessment of Microbial Demography.

Science.gov (United States)

Lapierre, Marguerite; Blin, Camille; Lambert, Amaury; Achaz, Guillaume; Rocha, Eduardo P C

2016-07-01

Recent studies have linked demographic changes and epidemiological patterns in bacterial populations using coalescent-based approaches. We identified 26 studies using skyline plots and found that 21 inferred overall population expansion. This surprising result led us to analyze the impact of natural selection, recombination (gene conversion), and sampling biases on demographic inference using skyline plots and site frequency spectra (SFS). Forward simulations based on biologically relevant parameters from Escherichia coli populations showed that theoretical arguments on the detrimental impact of recombination and especially natural selection on the reconstructed genealogies cannot be ignored in practice. In fact, both processes systematically lead to spurious interpretations of population expansion in skyline plots (and in SFS for selection). Weak purifying selection, and especially positive selection, had important effects on skyline plots, showing patterns akin to those of population expansions. State-of-the-art techniques to remove recombination further amplified these biases. We simulated three common sampling biases in microbiological research: uniform, clustered, and mixed sampling. Alone, or together with recombination and selection, they further mislead demographic inferences producing almost any possible skyline shape or SFS. Interestingly, sampling sub-populations also affected skyline plots and SFS, because the coalescent rates of populations and their sub-populations had different distributions. This study suggests that extreme caution is needed to infer demographic changes solely based on reconstructed genealogies. We suggest that the development of novel sampling strategies and the joint analyzes of diverse population genetic methods are strictly necessary to estimate demographic changes in populations where selection, recombination, and biased sampling are present. © The Author 2016. Published by Oxford University Press on behalf of the Society for

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.

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.

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.

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

Visual selective attention biases contribute to the other-race effect among 9-month-old infants.

Science.gov (United States)

Markant, Julie; Oakes, Lisa M; Amso, Dima

2016-04-01

During the first year of life, infants maintain their ability to discriminate faces from their own race but become less able to differentiate other-race faces. Though this is likely due to daily experience with own-race faces, the mechanisms linking repeated exposure to optimal face processing remain unclear. One possibility is that frequent experience with own-race faces generates a selective attention bias to these faces. Selective attention elicits enhancement of attended information and suppression of distraction to improve visual processing of attended objects. Thus attention biases to own-race faces may boost processing and discrimination of these faces relative to other-race faces. We used a spatial cueing task to bias attention to own- or other-race faces among Caucasian 9-month-old infants. Infants discriminated faces in the focus of the attention bias, regardless of race, indicating that infants remained sensitive to differences among other-race faces. Instead, efficacy of face discrimination reflected the extent of attention engagement. © 2015 Wiley Periodicals, Inc.

Omens of coupled model biases in the CMIP5 AMIP simulations

Science.gov (United States)

Găinuşă-Bogdan, Alina; Hourdin, Frédéric; Traore, Abdoul Khadre; Braconnot, Pascale

2018-02-01

Despite decades of efforts and improvements in the representation of processes as well as in model resolution, current global climate models still suffer from a set of important, systematic biases in sea surface temperature (SST), not much different from the previous generation of climate models. Many studies have looked at errors in the wind field, cloud representation or oceanic upwelling in coupled models to explain the SST errors. In this paper we highlight the relationship between latent heat flux (LH) biases in forced atmospheric simulations and the SST biases models develop in coupled mode, at the scale of the entire intertropical domain. By analyzing 22 pairs of forced atmospheric and coupled ocean-atmosphere simulations from the CMIP5 database, we show a systematic, negative correlation between the spatial patterns of these two biases. This link between forced and coupled bias patterns is also confirmed by two sets of dedicated sensitivity experiments with the IPSL-CM5A-LR model. The analysis of the sources of the atmospheric LH bias pattern reveals that the near-surface wind speed bias dominates the zonal structure of the LH bias and that the near-surface relative humidity dominates the east-west contrasts.

Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior.

Science.gov (United States)

Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan

2015-11-01

Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

Quantifying the impact of selection bias caused by nonparticipation in a case-control study of mobile phone use

DEFF Research Database (Denmark)

Vrijheid, Martine; Richardson, Lesley; Armstrong, Bruce K

2009-01-01

To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case-control study of mobile phone use and brain tumor.......To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case-control study of mobile phone use and brain tumor....

Selection bias in family reports on end of life with dementia in nursing homes

NARCIS (Netherlands)

van der Steen, J.T.; Deliens, L.; Ribbe, M.W.; Onwuteaka-Philipsen, B.D.

2012-01-01

Background: Selective participation in retrospective studies of families recruited after the patient's death may threaten generalizability of reports on end-of-life experiences. Objectives: To assess possible selection bias in retrospective study of dementia at the end of life using family reports.

Surface Winds and Dust Biases in Climate Models

Science.gov (United States)

Evan, A. T.

2018-01-01

An analysis of North African dust from models participating in the Fifth Climate Models Intercomparison Project (CMIP5) suggested that, when forced by observed sea surface temperatures, these models were unable to reproduce any aspects of the observed year-to-year variability in dust from North Africa. Consequently, there would be little reason to have confidence in the models' projections of changes in dust over the 21st century. However, no subsequent study has elucidated the root causes of the disagreement between CMIP5 and observed dust. Here I develop an idealized model of dust emission and then use this model to show that, over North Africa, such biases in CMIP5 models are due to errors in the surface wind fields and not due to the representation of dust emission processes. These results also suggest that because the surface wind field over North Africa is highly spatially autocorrelated, intermodel differences in the spatial structure of dust emission have little effect on the relative change in year-to-year dust emission over the continent. I use these results to show that similar biases in North African dust from the NASA Modern Era Retrospective analysis for Research and Applications (MERRA) version 2 surface wind field biases but that these wind biases were not present in the first version of MERRA.

Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

Science.gov (United States)

Zhao, Yusheng; Gowda, Manje; Longin, Friedrich H; Würschum, Tobias; Ranc, Nicolas; Reif, Jochen C

2012-08-01

Estimating marker effects based on routinely generated phenotypic data of breeding programs is a cost-effective strategy to implement genomic selection. Truncation selection in breeding populations, however, could have a strong impact on the accuracy to predict genomic breeding values. The main objective of our study was to investigate the influence of phenotypic selection on the accuracy and bias of genomic selection. We used experimental data of 788 testcross progenies from an elite maize breeding program. The testcross progenies were evaluated in unreplicated field trials in ten environments and fingerprinted with 857 SNP markers. Random regression best linear unbiased prediction method was used in combination with fivefold cross-validation based on genotypic sampling. We observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. In contrast, estimating marker effects based on bidirectional selected populations led to only a marginal decrease in the prediction accuracy of genomic breeding values. We concluded that bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs.

An evolutionary algorithm for model selection

Energy Technology Data Exchange (ETDEWEB)

Bicker, Karl [CERN, Geneva (Switzerland); Chung, Suh-Urk; Friedrich, Jan; Grube, Boris; Haas, Florian; Ketzer, Bernhard; Neubert, Sebastian; Paul, Stephan; Ryabchikov, Dimitry [Technische Univ. Muenchen (Germany)

2013-07-01

When performing partial-wave analyses of multi-body final states, the choice of the fit model, i.e. the set of waves to be used in the fit, can significantly alter the results of the partial wave fit. Traditionally, the models were chosen based on physical arguments and by observing the changes in log-likelihood of the fits. To reduce possible bias in the model selection process, an evolutionary algorithm was developed based on a Bayesian goodness-of-fit criterion which takes into account the model complexity. Starting from systematically constructed pools of waves which contain significantly more waves than the typical fit model, the algorithm yields a model with an optimal log-likelihood and with a number of partial waves which is appropriate for the number of events in the data. Partial waves with small contributions to the total intensity are penalized and likely to be dropped during the selection process, as are models were excessive correlations between single waves occur. Due to the automated nature of the model selection, a much larger part of the model space can be explored than would be possible in a manual selection. In addition the method allows to assess the dependence of the fit result on the fit model which is an important contribution to the systematic uncertainty.

A test of the critical assumption of the sensory bias model for the evolution of female mating preference using neural networks.

Science.gov (United States)

Fuller, Rebecca C

2009-07-01

The sensory bias model for the evolution of mating preferences states that mating preferences evolve as correlated responses to selection on nonmating behaviors sharing a common sensory system. The critical assumption is that pleiotropy creates genetic correlations that affect the response to selection. I simulated selection on populations of neural networks to test this. First, I selected for various combinations of foraging and mating preferences. Sensory bias predicts that populations with preferences for like-colored objects (red food and red mates) should evolve more readily than preferences for differently colored objects (red food and blue mates). Here, I found no evidence for sensory bias. The responses to selection on foraging and mating preferences were independent of one another. Second, I selected on foraging preferences alone and asked whether there were correlated responses for increased mating preferences for like-colored mates. Here, I found modest evidence for sensory bias. Selection for a particular foraging preference resulted in increased mating preference for similarly colored mates. However, the correlated responses were small and inconsistent. Selection on foraging preferences alone may affect initial levels of mating preferences, but these correlations did not constrain the joint evolution of foraging and mating preferences in these simulations.

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.

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.

Understanding the tropical warm temperature bias simulated by climate models

Science.gov (United States)

Brient, Florent; Schneider, Tapio

2017-04-01

The state-of-the-art coupled general circulation models have difficulties in representing the observed spatial pattern of surface tempertaure. A majority of them suffers a warm bias in the tropical subsiding regions located over the eastern parts of oceans. These regions are usually covered by low-level clouds scattered from stratus along the coasts to more vertically developed shallow cumulus farther from them. Models usually fail to represent accurately this transition. Here we investigate physical drivers of this warm bias in CMIP5 models through a near-surface energy budget perspective. We show that overestimated solar insolation due to a lack of stratocumulus mostly explains the warm bias. This bias also arises partly from inter-model differences in surface fluxes that could be traced to differences in near-surface relative humidity and air-sea temperature gradient. We investigate the role of the atmosphere in driving surface biases by comparing historical and atmopsheric (AMIP) experiments. We show that some differences in boundary-layer characteristics, mostly those related to cloud fraction and relative humidity, are already present in AMIP experiments and may be the drivers of coupled biases. This gives insights in how models can be improved for better simulations of the tropical climate.

Selection bias in genetic-epidemiological studies of cleft lip and palate

Energy Technology Data Exchange (ETDEWEB)

Christensen, K.; Holm, N.V.; Kock, K. (Odense Univ. (Denmark)); Olsen, J. (Aarhus Univ. (Denmark)); Fogh-Anderson, P.

1992-09-01

The possible impact of selection bias in genetic and epidemiological studies of cleft lip and palate was studied, using three nationwide ascertainment sources and an autopsy study in a 10% sample of the Danish population. A total of 670 cases were identified. Two national record systems, when used together, were found suitable for ascertaining facial cleft in live births. More than 95% ascertainment was obtained by means of surgical files for cleft lip (with or without cleft palate) without associated malformations/syndromes. However, surgical files could be a poor source for studying isolated cleft palate (CP) (only a 60% and biased ascertainment), and they cannot be used to study the prevalence of associated malformations or syndromes in facial cleft cases. The male:female ratio was 0.88 in surgically treated cases of CP and was 1.5 in nonoperated CP cases, making the overall sex ratio for CP 1.1 (95% confidence limits 0.86-1.4) The sex ratio for CP without associated malformation was 1.1 (95% confidence limits 0.84-1.6). One of the major test criteria in CP multifactorial threshold models (higher CP liability among male CP relatives) must be reconsidered, if other investigations confirm that a CP sex-ratio reversal to male predominance occurs when high ascertainment is achieved. 24 refs., 1 fig., 4 tabs.

Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias

KAUST Repository

Ma, Yanyuan

2013-09-01

We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we identify the efficient member that reaches the minimum possible estimation variance. The asymptotic properties and finite sample performance of the estimation and inference procedures are illustrated through theoretical analysis and simulations. A data example is also provided to illustrate the usefulness of the methods in practice. © 2013 American Statistical Association.

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).

Mixed signals: The effect of conflicting reward- and goal-driven biases on selective attention.

Science.gov (United States)

Preciado, Daniel; Munneke, Jaap; Theeuwes, Jan

2017-07-01

Attentional selection depends on the interaction between exogenous (stimulus-driven), endogenous (goal-driven), and selection history (experience-driven) factors. While endogenous and exogenous biases have been widely investigated, less is known about their interplay with value-driven attention. The present study investigated the interaction between reward-history and goal-driven biases on perceptual sensitivity (d') and response time (RT) in a modified cueing paradigm presenting two coloured cues, followed by sinusoidal gratings. Participants responded to the orientation of one of these gratings. In Experiment 1, one cue signalled reward availability but was otherwise task irrelevant. In Experiment 2, the same cue signalled reward, and indicated the target's most likely location at the opposite side of the display. This design introduced a conflict between reward-driven biases attracting attention and goal-driven biases directing it away. Attentional effects were examined comparing trials in which cue and target appeared at the same versus opposite locations. Two interstimulus interval (ISI) levels were used to probe the time course of attentional effects. Experiment 1 showed performance benefits at the location of the reward-signalling cue and costs at the opposite for both ISIs, indicating value-driven capture. Experiment 2 showed performance benefits only for the long ISI when the target was at the opposite to the reward-associated cue. At the short ISI, only performance costs were observed. These results reveal the time course of these biases, indicating that reward-driven effects influence attention early but can be overcome later by goal-driven control. This suggests that reward-driven biases are integrated as attentional priorities, just as exogenous and endogenous factors.

An Approach to Addressing Selection Bias in Survival Analysis

Science.gov (United States)

Carlin, Caroline S.; Solid, Craig A.

2014-01-01

This work proposes a frailty model that accounts for non-random treatment assignment in survival analysis. Using Monte Carlo simulation, we found that estimated treatment parameters from our proposed endogenous selection survival model (esSurv) closely parallel the consistent two-stage residual inclusion (2SRI) results, while offering computational and interpretive advantages. The esSurv method greatly enhances computational speed relative to 2SRI by eliminating the need for bootstrapped standard errors, and generally results in smaller standard errors than those estimated by 2SRI. In addition, esSurv explicitly estimates the correlation of unobservable factors contributing to both treatment assignment and the outcome of interest, providing an interpretive advantage over the residual parameter estimate in the 2SRI method. Comparisons with commonly used propensity score methods and with a model that does not account for non-random treatment assignment show clear bias in these methods that is not mitigated by increased sample size. We illustrate using actual dialysis patient data comparing mortality of patients with mature arteriovenous grafts for venous access to mortality of patients with grafts placed but not yet ready for use at the initiation of dialysis. We find strong evidence of endogeneity (with estimate of correlation in unobserved factors ρ̂ = 0.55), and estimate a mature-graft hazard ratio of 0.197 in our proposed method, with a similar 0.173 hazard ratio using 2SRI. The 0.630 hazard ratio from a frailty model without a correction for the non-random nature of treatment assignment illustrates the importance of accounting for endogeneity. PMID:24845211

Religious attendance after elevated depressive symptoms: is selection bias at work?

Directory of Open Access Journals (Sweden)

Lloyd Balbuena

2014-03-01

Full Text Available In an attempt to determine if selection bias could be a reason that religious attendance and depression are related, the predictive value of elevated depressive symptoms for a decrease in future attendance at religious services was examined in a longitudinal panel of 1,673 Dutch adults. Religious attendance was assessed yearly over five years using the single question, “how often do you attend religious gatherings nowadays?” Depressive symptoms were assessed four times within the first year using the Depression subscale of the Brief Symptom Inventory. Logistic regression models of change in attendance were created, stratifying by baseline attendance status. Attenders who developed elevated symptoms were less likely to subsequently decrease their attendance (relative risk ratio: 0.55, 95% CI [0.38–0.79] relative to baseline as compared to those without elevated symptoms. This inverse association remained significant after controlling for health and demographic covariates, and when using multiply imputed data to account for attrition. Non-attenders were unlikely to start attending after elevated depressive symptoms. This study provides counter evidence against previous findings that church attenders are a self-selected healthier group.

REAL-TIME ANALYSIS AND SELECTION BIASES IN THE SUPERNOVA LEGACY SURVEY

International Nuclear Information System (INIS)

Perrett, K.; Conley, A.; Carlberg, R.; Balam, D.; Hook, I. M.; Sullivan, M.; Pritchet, C.; Astier, P.; Balland, C.; Guy, J.; Hardin, D.; Pain, R.; Regnault, N.; Basa, S.; Fouchez, D.; Howell, D. A.

2010-01-01

The Supernova Legacy Survey (SNLS) has produced a high-quality, homogeneous sample of Type Ia supernovae (SNe Ia) out to redshifts greater than z = 1. In its first four years of full operation (to 2007 June), the SNLS discovered more than 3000 transient candidates, 373 of which have been spectroscopically confirmed as SNe Ia. Use of these SNe Ia in precision cosmology critically depends on an analysis of the observational biases incurred in the SNLS survey due to the incomplete sampling of the underlying SN Ia population. This paper describes our real-time supernova detection and analysis procedures, and uses detailed Monte Carlo simulations to examine the effects of Malmquist bias and spectroscopic sampling. Such sampling effects are found to become apparent at z ∼ 0.6, with a significant shift in the average magnitude of the spectroscopically confirmed SN Ia sample toward brighter values for z ∼> 0.75. We describe our approach to correct for these selection biases in our three-year SNLS cosmological analysis (SNLS3) and present a breakdown of the systematic uncertainties involved.

The Properties of Model Selection when Retaining Theory Variables

DEFF Research Database (Denmark)

Hendry, David F.; Johansen, Søren

Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....

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....

Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio

2015-01-01

The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman's two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail

2015-11-20

The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

The effect of mis-specification on mean and selection between the Weibull and lognormal models

Science.gov (United States)

Jia, Xiang; Nadarajah, Saralees; Guo, Bo

2018-02-01

The lognormal and Weibull models are commonly used to analyse data. Although selection procedures have been extensively studied, it is possible that the lognormal model could be selected when the true model is Weibull or vice versa. As the mean is important in applications, we focus on the effect of mis-specification on mean. The effect on lognormal mean is first considered if the lognormal sample is wrongly fitted by a Weibull model. The maximum likelihood estimate (MLE) and quasi-MLE (QMLE) of lognormal mean are obtained based on lognormal and Weibull models. Then, the impact is evaluated by computing ratio of biases and ratio of mean squared errors (MSEs) between MLE and QMLE. For completeness, the theoretical results are demonstrated by simulation studies. Next, the effect of the reverse mis-specification on Weibull mean is discussed. It is found that the ratio of biases and the ratio of MSEs are independent of the location and scale parameters of the lognormal and Weibull models. The influence could be ignored if some special conditions hold. Finally, a model selection method is proposed by comparing ratios concerning biases and MSEs. We also present a published data to illustrate the study in this paper.

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.

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

Probabilistic causality, selection bias, and the logic of the democratic peace

OpenAIRE

Slantchev, Branislav L; Alexandrova, A; Gartzke, E

2005-01-01

Rosato (2003) claims to have discredited democratic peace theories. However, the methodological approach adopted by the study cannot reliably generate the conclusions espoused by the author. Rosato seems to misunderstand the probabilistic nature of most arguments about democratic peace and ignores issues that an appropriate research design should account for. Further, the study's use of case studies and data sets without attention to selection-bias produces examples that actually support theo...

How and how much does RAD-seq bias genetic diversity estimates?

Science.gov (United States)

Cariou, Marie; Duret, Laurent; Charlat, Sylvain

2016-11-08

RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to "ideal" empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where

Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods

Science.gov (United States)

Hakala, Kirsti; Addor, Nans; Seibert, Jan

2017-04-01

Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of

Mechanisms of attentional selection bias for threatening emotions of anger and disgust in individuals with high-trait anxiety.

Science.gov (United States)

Xia, Luyao; Cui, Lixia; Zhang, Qin; Dong, Xiaofei; Shi, Guangyuan

2018-03-07

There are still some controversies that attentional bias to negative emotions in individuals with high-trait anxiety (HTA), as compare with those with low-trait anxiety (LTA), occurs in the engagement or disengagement facet of attentional selectivity and whether this attentional bias is affected by negative emotional types. In this study, we explored the different attentional selectivity mechanisms for threatening emotions of anger and disgust between individuals with HTA and LTA using the variant attentional-probe paradigm. The results showed that under the engagement condition, the HTA group's attentional bias index of the anger mood was negative and was significantly less than the disgusting mood (positive) and that the P1 was smaller with angry faces as compared with neutral faces, which was separate from the results of the disgusted faces, having a significant difference with neutral faces on P1 component. In the LTA group, under the disengagement condition, the attentional bias index of the disgusting mood was significantly bigger than the attentional bias index of the anger mood. Moreover, the P1 of the disgusted faces was significantly bigger than the P1 of the angry faces. The topographical maps were also made to reveal the different neural underpinnings. The results suggested that there were different mechanisms of selective attentional bias for threatening emotions of anger and disgust in individuals with HTA. HTA individuals were characterized by facilitated attentional engagement with angry faces and impaired attentional engagement with disgusted faces. LTA individuals had different neural underpinnings and had impaired attentional disengagement with disgusted faces.

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

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Boulesteix Anne-Laure

2009-12-01

Full Text Available Abstract Background In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data, since such analyses are particularly exposed to this kind of bias. Methods In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. Results We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case and the bias resulting from the choice of the classification method are examined both separately and jointly. Conclusions The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

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...

Anxiety-related biases in visual orienting and spatial motor response selection independently assessed by a probe-classification task

NARCIS (Netherlands)

Schrooten, M.G.S.; Smulders, F.T.Y.; Mogg, K.; Bradley, B.P.

2012-01-01

This dot-probe study assessed anxiety-related biases in visual attentional orienting and spatial motor response selection (motor attention) in high- and low-trait-anxious adults, and whether anxiety-related biases depend on response speed. Emotional-neutral word pairs appeared for 14 or 500 ms, with

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

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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

Uninformative polymorphisms bias genome scans for signatures of selection

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Roesti Marius

2012-06-01

Full Text Available Abstract Background With the establishment of high-throughput sequencing technologies and new methods for rapid and extensive single nucleotide (SNP discovery, marker-based genome scans in search of signatures of divergent selection between populations occupying ecologically distinct environments are becoming increasingly popular. Methods and Results On the basis of genome-wide SNP marker data generated by RAD sequencing of lake and stream stickleback populations, we show that the outcome of such studies can be systematically biased if markers with a low minor allele frequency are included in the analysis. The reason is that these ‘uninformative’ polymorphisms lack the adequate potential to capture signatures of drift and hitchhiking, the focal processes in ecological genome scans. Bias associated with uninformative polymorphisms is not eliminated by just avoiding technical artifacts in the data (PCR and sequencing errors, as a high proportion of SNPs with a low minor allele frequency is a general biological feature of natural populations. Conclusions We suggest that uninformative markers should be excluded from genome scans based on empirical criteria derived from careful inspection of the data, and that these criteria should be reported explicitly. Together, this should increase the quality and comparability of genome scans, and hence promote our understanding of the processes driving genomic differentiation.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

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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.

Overview about bias in Customer Satisfaction Surveys and focus on self-selection error

OpenAIRE

Giovanna Nicolini; Luciana Dalla Valle

2009-01-01

The present paper provides an overview of the main types of surveys carried out for customer satisfaction analyses. In order to carry out these surveys it is possible to plan a census or select a sample. The higher the accuracy of the survey, the more reliable the results of the analysis. For this very reason, researchers pay special attention to surveys with bias due to non sampling errors, in particular to self-selection errors. These phenomena are very frequent especially in web surveys. S...

An SIS model for cultural trait transmission with conformity bias.

Science.gov (United States)

Walters, Caroline E; Kendal, Jeremy R

2013-12-01

Epidemiological models have been applied to human health-related behaviors that are affected by social interaction. Typically these models have not considered conformity bias, that is, the exaggerated propensity to adopt commonly observed behaviors or opinions, or content biases, where the content of the learned trait affects the probability of adoption. Here we consider an interaction of these two effects, presenting an SIS-type model for the spread and persistence of a behavior which is transmitted via social learning. Uptake is controlled by a nonlinear dependence on the proportion of individuals demonstrating the behavior in a population. Three equilibrium solutions are found, their linear stability is analyzed and the results are compared with a model for unbiased social learning. Our analysis focuses on the effects of the strength of conformity bias and the effects of content biases which alter a conformity threshold frequency of the behavior, above which there is an exaggerated propensity for adoption. The strength of the conformity bias is found to qualitatively alter the predictions regarding whether the trait becomes endemic within the population and the proportion of individuals who display the trait when it is endemic. As the conformity strength increases, the number of feasible equilibrium solutions increases from two to three, leading to a situation where the stable equilibrium attained is dependent upon the initial state. Varying the conformity threshold frequency directionally alters the behavior invasion threshold. Finally we discuss the possible application of this model to binge drinking behavior. Copyright © 2013 Elsevier Inc. All rights reserved.

Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems

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Jianhu Zhao

2017-07-01

Full Text Available Airborne LiDAR bathymetry (ALB is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model.

Continuum model for chiral induced spin selectivity in helical molecules

Energy Technology Data Exchange (ETDEWEB)

Medina, Ernesto [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France); Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); González-Arraga, Luis A. [IMDEA Nanoscience, Cantoblanco, 28049 Madrid (Spain); Finkelstein-Shapiro, Daniel; Mujica, Vladimiro [Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); Berche, Bertrand [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France)

2015-05-21

A minimal model is exactly solved for electron spin transport on a helix. Electron transport is assumed to be supported by well oriented p{sub z} type orbitals on base molecules forming a staircase of definite chirality. In a tight binding interpretation, the spin-orbit coupling (SOC) opens up an effective π{sub z} − π{sub z} coupling via interbase p{sub x,y} − p{sub z} hopping, introducing spin coupled transport. The resulting continuum model spectrum shows two Kramers doublet transport channels with a gap proportional to the SOC. Each doubly degenerate channel satisfies time reversal symmetry; nevertheless, a bias chooses a transport direction and thus selects for spin orientation. The model predicts (i) which spin orientation is selected depending on chirality and bias, (ii) changes in spin preference as a function of input Fermi level and (iii) back-scattering suppression protected by the SO gap. We compute the spin current with a definite helicity and find it to be proportional to the torsion of the chiral structure and the non-adiabatic Aharonov-Anandan phase. To describe room temperature transport, we assume that the total transmission is the result of a product of coherent steps.

Evaluation of VIIRS AOD over North China Plain: biases from aerosol models

Science.gov (United States)

Zhu, J.; Xia, X.; Wang, J.; Chen, H.; Zhang, J.; Oo, M. M.; Holz, R.

2014-12-01

With the launch of the Visible Infrared Imaging Radiometer Suit (VIIRS) instrument onboard Suomi National Polar-orbiting Partnership(S-NPP) in late 2011, the aerosol products of VIIRS are receiving much attention.To date, mostevaluations of VIIRS aerosol productswere carried out about aerosol optical depth (AOD). To further assess the VIIRS AOD in China which is a heavy polluted region in the world,we made a comparison between VIIRS AOD and CE-318 radiometerobservation at the following three sites overNorth China Plain (NCP): metropolis-Beijing (AERONET), suburbs-XiangHe (AERONET) and regional background site- Xinglong (CARSNET).The results showed the VIIRS AOD at 550 nm has a positive mean bias error (MBE) of 0.14-0.15 and root mean square error (RMBE) 0.20. Among three sites, Beijing is mainly a source of bias with MBE 0.17-0.18 and RMBE 0.23-0.24, and this bias is larger than some recent global statics recently published in the literature. Further analysis shows that this large bias in VIIRS AOD overNCP may be partly caused by the aerosol model selection in VIIRS aerosol inversion. According to the retrieval of sky radiance from CE-318 at three sites, aerosols in NCP have high mean real part of refractive indices (1.52-1.53), large volume mean radius (0.17-0.18) and low concentration (0.04-0.09) of fine aerosol, and small mean radius (2.86-2.92) and high concentration (0.06-0.16) of coarse mode aerosol. These observation-based aerosol single scattering properties and size of fine and coarse aerosols differ fromthe aerosol properties used in VIIRSoperational algorithm.The dominant aerosol models used in VIIRS algorithm for these three sites are less polluted urban aerosol in Beijing and low-absorption smoke in other two sites, all of which don't agree with the high imaginary part of refractive indices from CE-318 retrieval. Therefore, the aerosol models in VIIRS algorithm are likely to be refined in NCP region.

Comparison of the linear bias models in the light of the Dark Energy Survey

Science.gov (United States)

Papageorgiou, A.; Basilakos, S.; Plionis, M.

2018-05-01

The evolution of the linear and scale independent bias, based on the most popular dark matter bias models within the Λ cold dark matter (ΛCDM) cosmology, is confronted to that of the Dark Energy Survey (DES) luminous red galaxies (LRGs). Applying a χ2 minimization procedure between models and data, we find that all the considered linear bias models reproduce well the LRG bias data. The differences among the bias models are absorbed in the predicted mass of the dark-matter halo in which LRGs live and which ranges between ˜6 × 1012 and 1.4 × 1013 h-1 M⊙, for the different bias models. Similar results, reaching however a maximum value of ˜2 × 1013 h-1 M⊙, are found by confronting the SDSS (2SLAQ) Large Red Galaxies clustering with theoretical clustering models, which also include the evolution of bias. This later analysis also provides a value of Ωm = 0.30 ± 0.01, which is in excellent agreement with recent joint analyses of different cosmological probes and the reanalysis of the Planck data.

Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality

Science.gov (United States)

Noh, Myoung-Jong; Howat, Ian M.

2018-02-01

The quality and efficiency of automated Digital Elevation Model (DEM) extraction from stereoscopic satellite imagery is critically dependent on the accuracy of the sensor model used for co-locating pixels between stereo-pair images. In the absence of ground control or manual tie point selection, errors in the sensor models must be compensated with increased matching search-spaces, increasing both the computation time and the likelihood of spurious matches. Here we present an algorithm for automatically determining and compensating the relative bias in Rational Polynomial Coefficients (RPCs) between stereo-pairs utilizing hierarchical, sub-pixel image matching in object space. We demonstrate the algorithm using a suite of image stereo-pairs from multiple satellites over a range stereo-photogrammetrically challenging polar terrains. Besides providing a validation of the effectiveness of the algorithm for improving DEM quality, experiments with prescribed sensor model errors yield insight into the dependence of DEM characteristics and quality on relative sensor model bias. This algorithm is included in the Surface Extraction through TIN-based Search-space Minimization (SETSM) DEM extraction software package, which is the primary software used for the U.S. National Science Foundation ArcticDEM and Reference Elevation Model of Antarctica (REMA) products.

Model Consistent Pseudo-Observations of Precipitation and Their Use for Bias Correcting Regional Climate Models

Directory of Open Access Journals (Sweden)

Peter Berg

2015-01-01

Full Text Available Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM problematic. We present a method to construct pseudo-observational precipitation data bymerging a large scale constrained RCMreanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models.

Selectivity and stoichiometry boosting of beta-cyclodextrin in cationic/anionic surfactant systems: when host-guest equilibrium meets biased aggregation equilibrium.

Science.gov (United States)

Jiang, Lingxiang; Yu, Caifang; Deng, Manli; Jin, Changwen; Wang, Yilin; Yan, Yun; Huang, Jianbin

2010-02-18

Cationic surfactant/anionic surfactant/beta-CD ternary aqueous systems provide a platform for the coexistence of the host-guest (beta-CD/surfactant) equilibrium and the biased aggregation (monomeric/aggregated surfactants) equilibrium. We report here that the interplay between the two equilibria dominates the systems as follows. (1) The biased aggregation equilibrium imposes an apparent selectivity on the host-guest equilibrium, namely, beta-CD has to always selectively bind the major surfactant (molar fraction > 0.5) even if binding constants of beta-CD to the pair of surfactants are quite similar. (2) In return, the host-guest equilibrium amplifies the bias of the aggregation equilibrium, that is, the selective binding partly removes the major surfactant from the aggregates and leaves the aggregate composition approaching the electroneutral mixing stoichiometry. (3) This composition variation enhances electrostatic attractions between oppositely charged surfactant head groups, thus resulting in less-curved aggregates. In particular, the present apparent host-guest selectivity is of remarkably high values, and the selectivity stems from the bias of the aggregation equilibrium rather than the difference in binding constants. Moreover, beta-CD is defined as a "stoichiometry booster" for the whole class of cationic/anionic surfactant systems, which provides an additional degree of freedom to directly adjust aggregate compositions of the systems. The stoichiometry boosting of the compositions can in turn affect or even determine microstructures and macroproperties of the systems.

The Difference-in-Difference Method: Assessing the Selection Bias in the Effects of Neighborhood Environment on Health

Science.gov (United States)

Grafova, Irina; Freedman, Vicki; Lurie, Nicole; Kumar, Rizie; Rogowski, Jeannette

2013-01-01

This paper uses the difference-in-difference estimation approach to explore the self-selection bias in estimating the effect of neighborhood economic environment on self-assessed health among older adults. The results indicate that there is evidence of downward bias in the conventional estimates of the effect of neighborhood economic disadvantage on self-reported health, representing a lower bound of the true effect. PMID:23623818

Active and Adaptive Learning from Biased Data with Applications in Astronomy

DEFF Research Database (Denmark)

Kremer, Jan

This thesis addresses the problem of machine learning from biased datasets in the context of astronomical applications. In astronomy there are many cases in which the training sample does not follow the true distribution. The thesis examines different types of biases and proposes algorithms...... set. Against this background, the thesis begins with a survey of active learning algorithms for the support vector machine. If the cost of additional labeling is prohibitive, unlabeled data can often be utilized instead and the sample selection bias can be overcome through domain adaptation, that is...... to handle them. During learning and when applying the predictive model, active learning enables algorithms to select training examples from a pool of unlabeled data and to request the labels. This allows for selecting examples that maximize the algorithm's accuracy despite an initial bias in the training...

Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models

Science.gov (United States)

Raykov, Tenko

2005-01-01

A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…

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)

Microcredit and domestic violence in Bangladesh: an exploration of selection bias influences.

Science.gov (United States)

Bajracharya, Ashish; Amin, Sajeda

2013-10-01

This article explores the relationship between women's participation in microcredit groups and domestic violence in Bangladesh. Several recent studies have raised concern about microcredit programs by reporting higher levels of violence among women who are members. These results, however, may be attributable to selection bias because members might differ from nonmembers in ways that make them more susceptible to violence to begin with. Using a sample of currently married women from the 2007 Bangladesh Demographic Health Survey (BDHS) (N = 4,195), we use propensity score matching (PSM) as a way of exploring selection bias in this relationship. Results suggest that the previously seen strong positive association between membership and violence does not hold when an appropriate comparison group, generated using PSM, is used in the analyses. Additional analyses also suggest that levels of violence do not differ significantly between members and nonmembers and instead could depend on context-specific factors related to poverty. Members for whom a match is not found report considerably higher levels of violence relative to nonmembers in the unmatched group. The background characteristics of members and nonmembers who do not match suggest that they are more likely to be younger and from relatively well-to-do households.

Sexual selection on male size drives the evolution of male-biased sexual size dimorphism via the prolongation of male development.

Science.gov (United States)

Rohner, Patrick T; Blanckenhorn, Wolf U; Puniamoorthy, Nalini

2016-06-01

Sexual size dimorphism (SSD) arises when the net effects of natural and sexual selection on body size differ between the sexes. Quantitative SSD variation between taxa is common, but directional intraspecific SSD reversals are rare. We combined micro- and macroevolutionary approaches to study geographic SSD variation in closely related black scavenger flies. Common garden experiments revealed stark intra- and interspecific variation: Sepsis biflexuosa is monomorphic across the Holarctic, while S. cynipsea (only in Europe) consistently exhibits female-biased SSD. Interestingly, S. neocynipsea displays contrasting SSD in Europe (females larger) and North America (males larger), a pattern opposite to the geographic reversal in SSD of S. punctum documented in a previous study. In accordance with the differential equilibrium model for the evolution of SSD, the intensity of sexual selection on male size varied between continents (weaker in Europe), whereas fecundity selection on female body size did not. Subsequent comparative analyses of 49 taxa documented at least six independent origins of male-biased SSD in Sepsidae, which is likely caused by sexual selection on male size and mediated by bimaturism. Therefore, reversals in SSD and the associated changes in larval development might be much more common and rapid and less constrained than currently assumed. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Impact of chlorophyll bias on the tropical Pacific mean climate in an earth system model

Science.gov (United States)

Lim, Hyung-Gyu; Park, Jong-Yeon; Kug, Jong-Seong

2017-12-01

Climate modeling groups nowadays develop earth system models (ESMs) by incorporating biogeochemical processes in their climate models. The ESMs, however, often show substantial bias in simulated marine biogeochemistry which can potentially introduce an undesirable bias in physical ocean fields through biogeophysical interactions. This study examines how and how much the chlorophyll bias in a state-of-the-art ESM affects the mean and seasonal cycle of tropical Pacific sea-surface temperature (SST). The ESM used in the present study shows a sizeable positive bias in the simulated tropical chlorophyll. We found that the correction of the chlorophyll bias can reduce the ESM's intrinsic cold SST mean bias in the equatorial Pacific. The biologically-induced cold SST bias is strongly affected by seasonally-dependent air-sea coupling strength. In addition, the correction of chlorophyll bias can improve the annual cycle of SST by up to 25%. This result suggests a possible modeling approach in understanding the two-way interactions between physical and chlorophyll biases by biogeophysical effects.

On the effect of deterministic terms on the bias in stable AR models

NARCIS (Netherlands)

van Giersbergen, N.P.A.

2004-01-01

This paper compares the first-order bias approximation for the autoregressive (AR) coefficients in stable AR models in the presence of deterministic terms. It is shown that the bias due to inclusion of an intercept and trend is twice as large as the bias due to an intercept. For the AR(1) model, the

Scanning of speechless comics changes spatial biases in mental model construction.

Science.gov (United States)

Román, Antonio; Flumini, Andrea; Santiago, Julio

2018-08-05

The mental representation of both time and number shows lateral spatial biases, which can be affected by habitual reading and writing direction. However, this effect is in place before children begin to read. One potential early cause is the experiences of looking at picture books together with a carer, as those images also follow the directionality of the script. What is the underlying mechanism for this effect? In the present study, we test the possibility that such experiences induce spatial biases in mental model construction, a mechanism which is a good candidate to induce the biases observed with numbers and times. We presented a speechless comic in either standard (left-to-right) or mirror-reversed (right-to-left) form to adult Spanish participants. We then asked them to draw the scene depicted by sentences like 'the square is between the cross and the circle'. The position of the lateral objects in these drawings reveals the spatial biases at work when building mental models in working memory. Under conditions of highly consistent directionality, the mirror comic changed pre-existing lateral biases. Processes of mental model construction in working memory stand as a potential mechanism for the generation of spatial biases for time and number.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'. © 2018 The Author(s).

Motion, identity and the bias toward agency

Directory of Open Access Journals (Sweden)

Chris eFields

2014-08-01

Full Text Available The well-documented human bias toward agency as a cause and therefore an explanation of observed events is typically attributed to evolutionary selection for a social brain. Based on a review of developmental and adult behavioral and neurocognitive data, it is argued that the bias toward agency is a result of the default human solution, developed during infancy, to the computational requirements of object re-identification over gaps in observation of more than a few seconds. If this model is correct, overriding the bias toward agency to construct mechanistic explanations of observed events requires structure-mapping inferences, implemented by the pre-motor action planning system, that replace agents with mechanisms as causes of unobserved changes in contextual or featural properties of objects. Experiments that would test this model are discussed.

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.

How to investigate and adjust for selection bias in cohort studies

DEFF Research Database (Denmark)

Nohr, Ellen A; Liew, Zeyan

2018-01-01

National Birth Cohort (DNBC) as examples on how to quantify selection bias and also understand the underlying selection mechanisms. Although women who chose to participate in the cohort were typically of higher social status, healthier and with less disease than all those eligible for study, differential...... of the presented methods are applicable even with limited data on non-participants and those lost to follow-up, and can also be applied to other study designs such as case-control studies and surveys. This article is protected by copyright. All rights reserved.......Longitudinal cohort studies can provide important evidence about preventable causes of disease, but the success relies heavily on the commitment of their participants, both at recruitment and during follow-up. Initial participation rates have decreased in recent decades as have willingness...

Impact of the choice of the precipitation reference data set on climate model selection and the resulting climate change signal

Science.gov (United States)

Gampe, D.; Ludwig, R.

2017-12-01

Regional Climate Models (RCMs) that downscale General Circulation Models (GCMs) are the primary tool to project future climate and serve as input to many impact models to assess the related changes and impacts under such climate conditions. Such RCMs are made available through the Coordinated Regional climate Downscaling Experiment (CORDEX). The ensemble of models provides a range of possible future climate changes around the ensemble mean climate change signal. The model outputs however are prone to biases compared to regional observations. A bias correction of these deviations is a crucial step in the impact modelling chain to allow the reproduction of historic conditions of i.e. river discharge. However, the detection and quantification of model biases are highly dependent on the selected regional reference data set. Additionally, in practice due to computational constraints it is usually not feasible to consider the entire ensembles of climate simulations with all members as input for impact models which provide information to support decision-making. Although more and more studies focus on model selection based on the preservation of the climate model spread, a selection based on validity, i.e. the representation of the historic conditions is still a widely applied approach. In this study, several available reference data sets for precipitation are selected to detect the model bias for the reference period 1989 - 2008 over the alpine catchment of the Adige River located in Northern Italy. The reference data sets originate from various sources, such as station data or reanalysis. These data sets are remapped to the common RCM grid at 0.11° resolution and several indicators, such as dry and wet spells, extreme precipitation and general climatology, are calculate to evaluate the capability of the RCMs to produce the historical conditions. The resulting RCM spread is compared against the spread of the reference data set to determine the related uncertainties and

Numerical modelling of pump limiter biasing on TEXTOR-94 and Tore Supra

International Nuclear Information System (INIS)

Gerhauser, H.; Claassen, H.A.; Mank, G.; Zagorski, R.; Loarer, T.; Gunn, J.; Boucher, C.

2002-01-01

The two-dimensional multifluid code TECXY has been used to model the biasing (with respect to the first wall) of the toroidal belt limiter ALT-II on the tokamak TEXTOR-94 and of the new toroidal pump limiter being installed on Tore Supra tokamak in the framework of the CIEL project. It is well known that the edge flow pattern can be influenced by the poloidal electric drifts from imposing radial electric fields. The modelling with TECXY introduces imprinted bias currents in the scrape-off layer (SOL) for the case of negative (limiter) biasing, and imprinted bias potentials for the case of positive biasing. This allowed us to simulate sufficiently well the experimental I-V characteristics for either biasing of ALT-II and also reproduced the essential features and trends of the observed plasma profiles in the SOL of TEXTOR-94. For negative biasing a moderate improvement of the pumping exhaust efficiency can be achieved in the case of TEXTOR. For Tore Supra, however, only a negligible improvement of the limiter performance with biasing can be predicted, which is explained by the relatively weak drift flows in Tore Supra. (author)

An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model

Energy Technology Data Exchange (ETDEWEB)

Patricola, Christina M.; Saravanan, R.; Hsieh, Jen-Shan [Texas A and M University, Department of Atmospheric Sciences, College Station, TX (United States); Li, Mingkui; Xu, Zhao [Texas A and M University, Department of Oceanography, College Station, TX (United States); Ocean University of China, Key Laboratory of Physical Oceanography of Ministry of Education, Qingdao (China); Chang, Ping [Texas A and M University, Department of Oceanography, College Station, TX (United States); Ocean University of China, Key Laboratory of Physical Oceanography of Ministry of Education, Qingdao (China); Second Institute of Oceanography, State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, Zhejiang (China)

2012-11-15

Coupled atmosphere-ocean general circulation models (AOGCMs) commonly fail to simulate the eastern equatorial Atlantic boreal summer cold tongue and produce a westerly equatorial trade wind bias. This tropical Atlantic bias problem is investigated with a high-resolution (27-km atmosphere represented by the Weather Research and Forecasting Model, 9-km ocean represented by the Regional Ocean Modeling System) coupled regional climate model. Uncoupled atmospheric simulations test climate sensitivity to cumulus, land-surface, planetary boundary layer, microphysics, and radiation parameterizations and reveal that the radiation scheme has a pronounced impact in the tropical Atlantic. The CAM radiation simulates a dry precipitation (up to -90%) and cold land-surface temperature (up to -8 K) bias over the Amazon related to an over-representation of low-level clouds and almost basin-wide westerly trade wind bias. The Rapid Radiative Transfer Model and Goddard radiation simulates doubled Amazon and Congo Basin precipitation rates and a weak eastern Atlantic trade wind bias. Season-long high-resolution coupled regional model experiments indicate that the initiation of the warm eastern equatorial Atlantic sea surface temperature (SST) bias is more sensitive to the local rather than basin-wide trade wind bias and to a wet Congo Basin instead of dry Amazon - which differs from AOGCM simulations. Comparisons between coupled and uncoupled simulations suggest a regional Bjerknes feedback confined to the eastern equatorial Atlantic amplifies the initial SST, wind, and deepened thermocline bias, while barrier layer feedbacks are relatively unimportant. The SST bias in some CRCM simulations resembles the typical AOGCM bias indicating that increasing resolution is unlikely a simple solution to this problem. (orig.)

Modeling the effect of selection history on pop-out visual search.

Directory of Open Access Journals (Sweden)

Yuan-Chi Tseng

Full Text Available While attentional effects in visual selection tasks have traditionally been assigned "top-down" or "bottom-up" origins, more recently it has been proposed that there are three major factors affecting visual selection: (1 physical salience, (2 current goals and (3 selection history. Here, we look further into selection history by investigating Priming of Pop-out (POP and the Distractor Preview Effect (DPE, two inter-trial effects that demonstrate the influence of recent history on visual search performance. Using the Ratcliff diffusion model, we model observed saccadic selections from an oddball search experiment that included a mix of both POP and DPE conditions. We find that the Ratcliff diffusion model can effectively model the manner in which selection history affects current attentional control in visual inter-trial effects. The model evidence shows that bias regarding the current trial's most likely target color is the most critical parameter underlying the effect of selection history. Our results are consistent with the view that the 3-item color-oddball task used for POP and DPE experiments is best understood as an attentional decision making task.

Model-based control of observer bias for the analysis of presence-only data in ecology.

Directory of Open Access Journals (Sweden)

David I Warton

Full Text Available Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record sightings at some locations than others (hereafter "observer bias". In this paper, we describe and evaluate a model-based approach to accounting for observer bias directly--by modelling presence locations as a function of known observer bias variables (such as accessibility variables in addition to environmental variables, then conditioning on a common level of bias to make predictions of species occurrence free of such observer bias. We implement this idea using point process models with a LASSO penalty, a new presence-only method related to maximum entropy modelling, that implicitly addresses the "pseudo-absence problem" of where to locate pseudo-absences (and how many. The proposed method of bias-correction is evaluated using systematically collected presence/absence data for 62 plant species endemic to the Blue Mountains near Sydney, Australia. It is shown that modelling and controlling for observer bias significantly improves the accuracy of predictions made using presence-only data, and usually improves predictions as compared to pseudo-absence or "inventory" methods of bias correction based on absences from non-target species. Future research will consider the potential for improving the proposed bias-correction approach by estimating the observer bias simultaneously across multiple species.

Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

Directory of Open Access Journals (Sweden)

Isaac Mugume

2016-01-01

Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity.

Directory of Open Access Journals (Sweden)

Nicolas Heslot

Full Text Available Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L. genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology and has formed the basis of most of our knowledge about cereals genetic diversity. This study compared GBS and DArT marker platforms for measuring genetic diversity and genomic selection (GS accuracy in elite U.S. soft winter wheat. From a set of 365 breeding lines, 38,412 single nucleotide polymorphism GBS markers were discovered and genotyped. The GBS SNPs gave a higher GS accuracy than 1,544 DArT markers on the same lines, despite 43.9% missing data. Using a bootstrap approach, we observed significantly more clustering of markers and ascertainment bias with DArT relative to GBS. The minor allele frequency distribution of GBS markers had a deficit of rare variants compared to DArT markers. Despite the ascertainment bias of the DArT markers, GS accuracy for three traits out of four was not significantly different when an equal number of markers were used for each platform. This suggests that the gain in accuracy observed using GBS compared to DArT markers was mainly due to a large increase in the number of markers available for the analysis.

Impact of Marker Ascertainment Bias on Genomic Selection Accuracy and Estimates of Genetic Diversity

Science.gov (United States)

Heslot, Nicolas; Rutkoski, Jessica; Poland, Jesse; Jannink, Jean-Luc; Sorrells, Mark E.

2013-01-01

Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS) is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology) and has formed the basis of most of our knowledge about cereals genetic diversity. This study compared GBS and DArT marker platforms for measuring genetic diversity and genomic selection (GS) accuracy in elite U.S. soft winter wheat. From a set of 365 breeding lines, 38,412 single nucleotide polymorphism GBS markers were discovered and genotyped. The GBS SNPs gave a higher GS accuracy than 1,544 DArT markers on the same lines, despite 43.9% missing data. Using a bootstrap approach, we observed significantly more clustering of markers and ascertainment bias with DArT relative to GBS. The minor allele frequency distribution of GBS markers had a deficit of rare variants compared to DArT markers. Despite the ascertainment bias of the DArT markers, GS accuracy for three traits out of four was not significantly different when an equal number of markers were used for each platform. This suggests that the gain in accuracy observed using GBS compared to DArT markers was mainly due to a large increase in the number of markers available for the analysis. PMID:24040295

Model for an irreversible bias current in the superconducting qubit measurement process

International Nuclear Information System (INIS)

Hutchinson, G. D.; Williams, D. A.; Holmes, C. A.; Stace, T. M.; Spiller, T. P.; Barrett, S. D.; Milburn, G. J.; Hasko, D. G.

2006-01-01

The superconducting charge-phase ''quantronium'' qubit is considered in order to develop a model for the measurement process used in the experiment of Vion et al. [Science 296, 886 (2002)]. For this model we propose a method for including the bias current in the readout process in a fundamentally irreversible way, which to first order is approximated by the Josephson junction tilted-washboard potential phenomenology. The decohering bias current is introduced in the form of a Lindblad operator and the Wigner function for the current-biased readout Josephson junction is derived and analyzed. During the readout current pulse used in the quantronium experiment we find that the coherence of the qubit initially prepared in a symmetric superposition state is lost at a time of 0.2 ns after the bias current pulse has been applied, a time scale that is much shorter than the experimental readout time. Additionally we look at the effect of Johnson-Nyquist noise with zero mean from the current source during the qubit manipulation and show that the decoherence due to the irreversible bias current description is an order of magnitude smaller than that found through adding noise to the reversible tilted-washboard potential model. Our irreversible bias current model is also applicable to persistent-current-based qubits where the state is measured according to its flux via a small-inductance direct-current superconducting quantum interference device

Estimating Gravity Biases with Wavelets in Support of a 1-cm Accurate Geoid Model

Science.gov (United States)

Ahlgren, K.; Li, X.

2017-12-01

Systematic errors that reside in surface gravity datasets are one of the major hurdles in constructing a high-accuracy geoid model at high resolutions. The National Oceanic and Atmospheric Administration's (NOAA) National Geodetic Survey (NGS) has an extensive historical surface gravity dataset consisting of approximately 10 million gravity points that are known to have systematic biases at the mGal level (Saleh et al. 2013). As most relevant metadata is absent, estimating and removing these errors to be consistent with a global geopotential model and airborne data in the corresponding wavelength is quite a difficult endeavor. However, this is crucial to support a 1-cm accurate geoid model for the United States. With recently available independent gravity information from GRACE/GOCE and airborne gravity from the NGS Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project, several different methods of bias estimation are investigated which utilize radial basis functions and wavelet decomposition. We estimate a surface gravity value by incorporating a satellite gravity model, airborne gravity data, and forward-modeled topography at wavelet levels according to each dataset's spatial wavelength. Considering the estimated gravity values over an entire gravity survey, an estimate of the bias and/or correction for the entire survey can be found and applied. In order to assess the accuracy of each bias estimation method, two techniques are used. First, each bias estimation method is used to predict the bias for two high-quality (unbiased and high accuracy) geoid slope validation surveys (GSVS) (Smith et al. 2013 & Wang et al. 2017). Since these surveys are unbiased, the various bias estimation methods should reflect that and provide an absolute accuracy metric for each of the bias estimation methods. Secondly, the corrected gravity datasets from each of the bias estimation methods are used to build a geoid model. The accuracy of each geoid model

Significance of Bias Correction in Drought Frequency and Scenario Analysis Based on Climate Models

Science.gov (United States)

Aryal, Y.; Zhu, J.

2015-12-01

Assessment of future drought characteristics is difficult as climate models usually have bias in simulating precipitation frequency and intensity. To overcome this limitation, output from climate models need to be bias corrected based on the specific purpose of applications. In this study, we examine the significance of bias correction in the context of drought frequency and scenario analysis using output from climate models. In particular, we investigate the performance of three widely used bias correction techniques: (1) monthly bias correction (MBC), (2) nested bias correction (NBC), and (3) equidistance quantile mapping (EQM) The effect of bias correction in future scenario of drought frequency is also analyzed. The characteristics of drought are investigated in terms of frequency and severity in nine representative locations in different climatic regions across the United States using regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP). The Standardized Precipitation Index (SPI) is used as the means to compare and forecast drought characteristics at different timescales. Systematic biases in the RCM precipitation output are corrected against the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data. The results demonstrate that bias correction significantly decreases the RCM errors in reproducing drought frequency derived from the NARR data. Preserving mean and standard deviation is essential for climate models in drought frequency analysis. RCM biases both have regional and timescale dependence. Different timescale of input precipitation in the bias corrections show similar results. Drought frequency obtained from the RCM future (2040-2070) scenarios is compared with that from the historical simulations. The changes in drought characteristics occur in all climatic regions. The relative changes in drought frequency in future scenario in relation to

High Precision Clock Bias Prediction Model in Clock Synchronization System

Directory of Open Access Journals (Sweden)

Zan Liu

2016-01-01

Full Text Available Time synchronization is a fundamental requirement for many services provided by a distributed system. Clock calibration through the time signal is the usual way to realize the synchronization among the clocks used in the distributed system. The interference to time signal transmission or equipment failures may bring about failure to synchronize the time. To solve this problem, a clock bias prediction module is paralleled in the clock calibration system. And for improving the precision of clock bias prediction, the first-order grey model with one variable (GM(1,1 model is proposed. In the traditional GM(1,1 model, the combination of parameters determined by least squares criterion is not optimal; therefore, the particle swarm optimization (PSO is used to optimize GM(1,1 model. At the same time, in order to avoid PSO getting stuck at local optimization and improve its efficiency, the mechanisms that double subgroups and nonlinear decreasing inertia weight are proposed. In order to test the precision of the improved model, we design clock calibration experiments, where time signal is transferred via radio and wired channel, respectively. The improved model is built on the basis of clock bias acquired in the experiments. The results show that the improved model is superior to other models both in precision and in stability. The precision of improved model increased by 66.4%~76.7%.

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.

An evaluation of bias in propensity score-adjusted non-linear regression models.

Science.gov (United States)

Wan, Fei; Mitra, Nandita

2018-03-01

Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.

Perceptual Color Space Representations in the Oculomotor System Are Modulated by Surround Suppression and Biased Selection

OpenAIRE

Kehoe, Devin H.; Rahimi, Maryam; Fallah, Mazyar

2018-01-01

The oculomotor system utilizes color extensively for planning saccades. Therefore, we examined how the oculomotor system actually encodes color and several factors that modulate these representations: attention-based surround suppression and inherent biases in selecting and encoding color categories. We measured saccade trajectories while human participants performed a memory-guided saccade task with color targets and distractors and examined whether oculomotor target selection processing was...

Correcting Biases in a lower resolution global circulation model with data assimilation

Science.gov (United States)

Canter, Martin; Barth, Alexander

2016-04-01

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model's equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have

Drought Duration Biases in Current Global Climate Models

Science.gov (United States)

Moon, Heewon; Gudmundsson, Lukas; Seneviratne, Sonia

2016-04-01

Several droughts in the recent past are characterized by their increased duration and intensity. In particular, substantially prolonged droughts have brought major societal and economic losses in certain regions, yet climate change projections of such droughts in terms of duration is subject to large uncertainties. This study analyzes the biases of drought duration in state-of-the-art global climate model (GCM) simulations from the 5th phase of Coupled Model Intercomparison Project (CMIP5). Drought durations are defined as negative precipitation anomalies and evaluated with three observation-based datasets in the period of 1901-2010. Large spread in biases of GCMs is commonly found in all regions, with particular strong biases in North East Brazil, Africa, Northern Australia, Central America, Central and Northern Europe, Sahel and Asia. Also in most regions, the interquartile range of bias lies below 0, meaning that the GCMs tend to underestimate drought durations. Meanwhile in some regions such as Western South America, the Amazon, Sahel, West and South Africa, and Asia, considerable inconsistency among the three observation-based datasets were found. These results indicate substantial uncertainties and errors in current GCMs for simulating drought durations as well as a large spread in observation-based datasets, both of which are found to be particularly strong in those regions that are often considered to be hot spots of projected future drying. The underlying sources of these uncertainties need to be identified in further study and will be applied to constrain GCM-based drought projections under climate change.

Occupational noise exposure and age correction: the problem of selection bias.

Science.gov (United States)

Dobie, Robert A

2009-12-01

Selection bias often invalidates conclusions about populations based on clinical convenience samples. A recent paper in this journal makes two surprising assertions about noise-induced permanent threshold shift (NIPTS): first, that there is more NIPTS at 2 kHz than at higher frequencies; second, that NIPTS declines with advancing age. Neither assertion can be supported with the data presented, which were obtained from a clinical sample; both are consistent with the hypothesis that people who choose to attend an audiology clinic have worse hearing, especially at 2 kHz, than people of the same age and gender who choose not to attend.

A Model of Gravity Vector Measurement Noise for Estimating Accelerometer Bias in Gravity Disturbance Compensation.

Science.gov (United States)

Tie, Junbo; Cao, Juliang; Chang, Lubing; Cai, Shaokun; Wu, Meiping; Lian, Junxiang

2018-03-16

Compensation of gravity disturbance can improve the precision of inertial navigation, but the effect of compensation will decrease due to the accelerometer bias, and estimation of the accelerometer bias is a crucial issue in gravity disturbance compensation. This paper first investigates the effect of accelerometer bias on gravity disturbance compensation, and the situation in which the accelerometer bias should be estimated is established. The accelerometer bias is estimated from the gravity vector measurement, and a model of measurement noise in gravity vector measurement is built. Based on this model, accelerometer bias is separated from the gravity vector measurement error by the method of least squares. Horizontal gravity disturbances are calculated through EGM2008 spherical harmonic model to build the simulation scene, and the simulation results indicate that precise estimations of the accelerometer bias can be obtained with the proposed method.

A Model of Gravity Vector Measurement Noise for Estimating Accelerometer Bias in Gravity Disturbance Compensation

Science.gov (United States)

Cao, Juliang; Cai, Shaokun; Wu, Meiping; Lian, Junxiang

2018-01-01

Compensation of gravity disturbance can improve the precision of inertial navigation, but the effect of compensation will decrease due to the accelerometer bias, and estimation of the accelerometer bias is a crucial issue in gravity disturbance compensation. This paper first investigates the effect of accelerometer bias on gravity disturbance compensation, and the situation in which the accelerometer bias should be estimated is established. The accelerometer bias is estimated from the gravity vector measurement, and a model of measurement noise in gravity vector measurement is built. Based on this model, accelerometer bias is separated from the gravity vector measurement error by the method of least squares. Horizontal gravity disturbances are calculated through EGM2008 spherical harmonic model to build the simulation scene, and the simulation results indicate that precise estimations of the accelerometer bias can be obtained with the proposed method. PMID:29547552

Large biases in regression-based constituent flux estimates: causes and diagnostic tools

Science.gov (United States)

Hirsch, Robert M.

2014-01-01

It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

Causes of model dry and warm bias over central U.S. and impact on climate projections.

Science.gov (United States)

Lin, Yanluan; Dong, Wenhao; Zhang, Minghua; Xie, Yuanyu; Xue, Wei; Huang, Jianbin; Luo, Yong

2017-10-12

Climate models show a conspicuous summer warm and dry bias over the central United States. Using results from 19 climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), we report a persistent dependence of warm bias on dry bias with the precipitation deficit leading the warm bias over this region. The precipitation deficit is associated with the widespread failure of models in capturing strong rainfall events in summer over the central U.S. A robust linear relationship between the projected warming and the present-day warm bias enables us to empirically correct future temperature projections. By the end of the 21st century under the RCP8.5 scenario, the corrections substantially narrow the intermodel spread of the projections and reduce the projected temperature by 2.5 K, resulting mainly from the removal of the warm bias. Instead of a sharp decrease, after this correction the projected precipitation is nearly neutral for all scenarios.Climate models repeatedly show a warm and dry bias over the central United States, but the origin of this bias remains unclear. Here the authors associate this bias to precipitation deficits in models and after applying a correction, projected precipitation in this region shows no significant changes.

A regional model simulation of the 1991 severe precipitation event over the Yangtze-Huai River Valley. Part 2: Model bias

Energy Technology Data Exchange (ETDEWEB)

Gong, W.; Wang, W.C.

2000-01-01

This is the second part of a study investigating the 1991 severe precipitation event over the Uangtze-Huai River valley (YHRV) in China using both observations and regional model simulations. While Part 1 reported on the Mei-yu front and its association with large-scale circulation, this study documents the biases associated with the treatment of the lateral boundary in the regional model. Two aspects of the biases were studied: the driving field, which provides large-scale boundary forcing, and the coupling scheme, which specified how the forcing is adopted by the model. The former bias is defined as model uncertainty because it is not related to the model itself, while the latter bias (as well as those biases attributed to other sources) is referred to as model error. These two aspects were examined by analyzing the regional model simulations of the 1991 summer severe precipitation event over YHRV using different driving fields (ECMWF-TOGA objective analysis, ECMWF reanalysis, and NCEP-NCAR reanalysis) and coupling scheme (distribution function of the nudging coefficient and width of the buffer zone). Spectral analysis was also used to study the frequency distribution of the bias.

Model selection and inference a practical information-theoretic approach

CERN Document Server

Burnham, Kenneth P

1998-01-01

This book is unique in that it covers the philosophy of model-based data analysis and an omnibus strategy for the analysis of empirical data The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data Kullback-Leibler information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information This leads to Akaike's Information Criterion (AIC) and various extensions and these are relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are ...

Biased Type 1 Cannabinoid Receptor Signaling Influences Neuronal Viability in a Cell Culture Model of Huntington Disease.

Science.gov (United States)

Laprairie, Robert B; Bagher, Amina M; Kelly, Melanie E M; Denovan-Wright, Eileen M

2016-03-01

Huntington disease (HD) is an inherited, autosomal dominant, neurodegenerative disorder with limited treatment options. Prior to motor symptom onset or neuronal cell loss in HD, levels of the type 1 cannabinoid receptor (CB1) decrease in the basal ganglia. Decreasing CB1 levels are strongly correlated with chorea and cognitive deficit. CB1 agonists are functionally selective (biased) for divergent signaling pathways. In this study, six cannabinoids were tested for signaling bias in in vitro models of medium spiny projection neurons expressing wild-type (STHdh(Q7/Q7)) or mutant huntingtin protein (STHdh(Q111/Q111)). Signaling bias was assessed using the Black and Leff operational model. Relative activity [ΔlogR (τ/KA)] and system bias (ΔΔlogR) were calculated relative to the reference compound WIN55,212-2 for Gαi/o, Gαs, Gαq, Gβγ, and β-arrestin1 signaling following treatment with 2-arachidonoylglycerol (2-AG), anandamide (AEA), CP55,940, Δ(9)-tetrahydrocannabinol (THC), cannabidiol (CBD), and THC+CBD (1:1), and compared between wild-type and HD cells. The Emax of Gαi/o-dependent extracellular signal-regulated kinase (ERK) signaling was 50% lower in HD cells compared with wild-type cells. 2-AG and AEA displayed Gαi/o/Gβγ bias and normalized CB1 protein levels and improved cell viability, whereas CP55,940 and THC displayed β-arrestin1 bias and reduced CB1 protein levels and cell viability in HD cells. CBD was not a CB1 agonist but inhibited THC-dependent signaling (THC+CBD). Therefore, enhancing Gαi/o-biased endocannabinoid signaling may be therapeutically beneficial in HD. In contrast, cannabinoids that are β-arrestin-biased--such as THC found at high levels in modern varieties of marijuana--may be detrimental to CB1 signaling, particularly in HD where CB1 levels are already reduced. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

Analysis of bias in groundwater modelling due to the interpretation of site characterization data

International Nuclear Information System (INIS)

Clark, K.J.; Impey, M.D.; Ikeda, T.; McEwen, T.; White, M.

1997-01-01

Bias is a difference between model and reality. Bias can be introduced at any stage of the modelling process during a site characterization or performance assessment program. It is desirable to understand such bias so as to be able to optimally design and interpret a site characterization program. The objective of this study was to examine the source and effect of bias due to the assumptions modellers have to make because reality cannot be fully characterized in the prediction of groundwater fluxes. A well-defined synthetic reality was therefore constructed for this study. A limited subset of these data were independently interpreted and used to compute groundwater fluxes across specified boundaries in a cross section. The modelling results were compared to the true solutions derived using the full dataset. This study clarified and identified the large number of assumptions and judgments which have to be made when modelling a limited site characterization dataset. It is concluded that bias is introduced at each modelling stage, and that it is not necessarily detectable by the modellers even if multiple runs with varied parameter values are undertaken

Anticlockwise or Clockwise? A Dynamic Perception-Action-Laterality Model for Directionality Bias in Visuospatial Functioning

Science.gov (United States)

Karim, A.K.M. Rezaul; Proulx, Michael J.; Likova, Lora T.

2016-01-01

Reviewing the relevant literature in visual psychophysics and visual neuroscience we propose a three-stage model of directionality bias in visuospatial functioning. We call this model the ‘Perception-Action-Laterality’ (PAL) hypothesis. We analyzed the research findings for a wide range of visuospatial tasks, showing that there are two major directionality trends: clockwise versus anticlockwise. It appears these preferences are combinatorial, such that a majority of people fall in the first category demonstrating a preference for stimuli/objects arranged from left-to-right rather than from right-to-left, while people in the second category show an opposite trend. These perceptual biases can guide sensorimotor integration and action, creating two corresponding turner groups in the population. In support of PAL, we propose another model explaining the origins of the biases– how the neurogenetic factors and the cultural factors interact in a biased competition framework to determine the direction and extent of biases. This dynamic model can explain not only the two major categories of biases, but also the unbiased, unreliably biased or mildly biased cases in visuosptial functioning. PMID:27350096

Probabilistic Multiple-Bias Modeling Applied to the Canadian Data From the Interphone Study of Mobile Phone Use and Risk of Glioma, Meningioma, Acoustic Neuroma, and Parotid Gland Tumors.

Science.gov (United States)

Momoli, F; Siemiatycki, J; McBride, M L; Parent, M-É; Richardson, L; Bedard, D; Platt, R; Vrijheid, M; Cardis, E; Krewski, D

2017-10-01

We undertook a re-analysis of the Canadian data from the 13-country case-control Interphone Study (2001-2004), in which researchers evaluated the associations of mobile phone use with the risks of brain, acoustic neuroma, and parotid gland tumors. In the main publication of the multinational Interphone Study, investigators concluded that biases and errors prevented a causal interpretation. We applied a probabilistic multiple-bias model to address possible biases simultaneously, using validation data from billing records and nonparticipant questionnaires as information on recall error and selective participation. In our modeling, we sought to adjust for these sources of uncertainty and to facilitate interpretation. For glioma, when comparing those in the highest quartile of use (>558 lifetime hours) to those who were not regular users, the odds ratio was 2.0 (95% confidence interval: 1.2, 3.4). After adjustment for selection and recall biases, the odds ratio was 2.2 (95% limits: 1.3, 4.1). There was little evidence of an increase in the risk of meningioma, acoustic neuroma, or parotid gland tumors in relation to mobile phone use. Adjustments for selection and recall biases did not materially affect interpretation in our results from Canadian data. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Hidden multidimensional social structure modeling applied to biased social perception

Science.gov (United States)

Maletić, Slobodan; Zhao, Yi

2018-02-01

Intricacies of the structure of social relations are realized by representing a collection of overlapping opinions as a simplicial complex, thus building latent multidimensional structures, through which agents are, virtually, moving as they exchange opinions. The influence of opinion space structure on the distribution of opinions is demonstrated by modeling consensus phenomena when the opinion exchange between individuals may be affected by the false consensus effect. The results indicate that in the cases with and without bias, the road toward consensus is influenced by the structure of multidimensional space of opinions, and in the biased case, complete consensus is achieved. The applications of proposed modeling framework can easily be generalized, as they transcend opinion formation modeling.

The relative age effect in the German Football TID Programme: biases in motor performance diagnostics and effects on single motor abilities and skills in groups of selected players.

Science.gov (United States)

Votteler, Andreas; Höner, Oliver

2014-01-01

This study examined the disturbing effects of relative age on the talent identification process in the talent development programme of the German Football Association. The bias in the selection rate was examined via the extent of relative age effects. The bias in motor performance diagnostics was analysed by comparing the motor performance of selected players with normal motor development. The mechanisms underlying the relative age biases in motor performance were examined by modelling the direct and indirect effects of relative age on single motor performance tests for sprint, running agility, dribbling and ball passing and control. Data from 10,130 selected football players from the U12 to U15 age groups were collected in autumn 2010. The birth distribution differed significantly from the reference population with approximately 61% of the players born in the first half of the year. The selection probability was approximately two times higher for players born in the first quarter of the year than for players born in the last quarter. Revised motor performance diagnostics showed better results on average for relatively younger players. Path analysis revealed significant direct and indirect relative age effects for physiologically demanding tests and almost no effects for technically demanding tests. Large sample sizes allowed high resolution in relative age with additional informational content and multivariate modelling of the complex relationships among relative age, physical development and motor performance. The results are discussed on how relative age affects the effectiveness and fairness of talent identification and development processes.

Exploring the common molecular basis for the universal DNA mutation bias: Revival of Loewdin mutation model

International Nuclear Information System (INIS)

Fu, Liang-Yu; Wang, Guang-Zhong; Ma, Bin-Guang; Zhang, Hong-Yu

2011-01-01

Highlights: → There exists a universal G:C → A:T mutation bias in three domains of life. → This universal mutation bias has not been sufficiently explained. → A DNA mutation model proposed by Loewdin 40 years ago offers a common explanation. -- Abstract: Recently, numerous genome analyses revealed the existence of a universal G:C → A:T mutation bias in bacteria, fungi, plants and animals. To explore the molecular basis for this mutation bias, we examined the three well-known DNA mutation models, i.e., oxidative damage model, UV-radiation damage model and CpG hypermutation model. It was revealed that these models cannot provide a sufficient explanation to the universal mutation bias. Therefore, we resorted to a DNA mutation model proposed by Loewdin 40 years ago, which was based on inter-base double proton transfers (DPT). Since DPT is a fundamental and spontaneous chemical process and occurs much more frequently within GC pairs than AT pairs, Loewdin model offers a common explanation for the observed universal mutation bias and thus has broad biological implications.

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.

Model selection with multiple regression on distance matrices leads to incorrect inferences.

Directory of Open Access Journals (Sweden)

Ryan P Franckowiak

Full Text Available In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC, its small-sample correction (AICc, and the Bayesian information criterion (BIC to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

Science.gov (United States)

Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

2007-12-01

Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

Item bias detection in the Hospital Anxiety and Depression Scale using structural equation modeling: comparison with other item bias detection methods

NARCIS (Netherlands)

Verdam, M.G.E.; Oort, F.J.; Sprangers, M.A.G.

Purpose Comparison of patient-reported outcomes may be invalidated by the occurrence of item bias, also known as differential item functioning. We show two ways of using structural equation modeling (SEM) to detect item bias: (1) multigroup SEM, which enables the detection of both uniform and

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

Science.gov (United States)

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

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.

Reduction of bias in neutron multiplicity assay using a weighted point model

Energy Technology Data Exchange (ETDEWEB)

Geist, W. H. (William H.); Krick, M. S. (Merlyn S.); Mayo, D. R. (Douglas R.)

2004-01-01

Accurate assay of most common plutonium samples was the development goal for the nondestructive assay technique of neutron multiplicity counting. Over the past 20 years the technique has been proven for relatively pure oxides and small metal items. Unfortunately, the technique results in large biases when assaying large metal items. Limiting assumptions, such as unifoh multiplication, in the point model used to derive the multiplicity equations causes these biases for large dense items. A weighted point model has been developed to overcome some of the limitations in the standard point model. Weighting factors are detemiined from Monte Carlo calculations using the MCNPX code. Monte Carlo calculations give the dependence of the weighting factors on sample mass and geometry, and simulated assays using Monte Carlo give the theoretical accuracy of the weighted-point-model assay. Measured multiplicity data evaluated with both the standard and weighted point models are compared to reference values to give the experimental accuracy of the assay. Initial results show significant promise for the weighted point model in reducing or eliminating biases in the neutron multiplicity assay of metal items. The negative biases observed in the assay of plutonium metal samples are caused by variations in the neutron multiplication for neutrons originating in various locations in the sample. The bias depends on the mass and shape of the sample and depends on the amount and energy distribution of the ({alpha},n) neutrons in the sample. When the standard point model is used, this variable-multiplication bias overestimates the multiplication and alpha values of the sample, and underestimates the plutonium mass. The weighted point model potentially can provide assay accuracy of {approx}2% (1 {sigma}) for cylindrical plutonium metal samples < 4 kg with {alpha} < 1 without knowing the exact shape of the samples, provided that the ({alpha},n) source is uniformly distributed throughout the

Facial expression judgments support a socio-relational model, rather than a negativity bias model of political psychology.

Science.gov (United States)

Vigil, Jacob M; Strenth, Chance

2014-06-01

Self-reported opinions and judgments may be more rooted in expressive biases than in cognitive processing biases, and ultimately operate within a broader behavioral style for advertising the capacity - versus the trustworthiness - dimension of human reciprocity potential. Our analyses of facial expression judgments of likely voters are consistent with this thesis, and directly contradict one major prediction from the authors' "negativity-bias" model.

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.

Association between mean and interannual equatorial Indian Ocean subsurface temperature bias in a coupled model

Science.gov (United States)

Srinivas, G.; Chowdary, Jasti S.; Gnanaseelan, C.; Prasad, K. V. S. R.; Karmakar, Ananya; Parekh, Anant

2018-03-01

In the present study the association between mean and interannual subsurface temperature bias over the equatorial Indian Ocean (EIO) is investigated during boreal summer (June through September; JJAS) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. Anomalously high subsurface warm bias (greater than 3 °C) over the eastern EIO (EEIO) region is noted in CFSv2 during summer, which is higher compared to other parts of the tropical Indian Ocean. Prominent eastward current bias in the upper 100 m over the EIO region induced by anomalous westerly winds is primarily responsible for subsurface temperature bias. The eastward currents transport warm water to the EEIO and is pushed down to subsurface due to downwelling. Thus biases in both horizontal and vertical currents over the EIO region support subsurface warm bias. The evolution of systematic subsurface warm bias in the model shows strong interannual variability. These maximum subsurface warming episodes over the EEIO are mainly associated with La Niña like forcing. Strong convergence of low level winds over the EEIO and Maritime continent enhanced the westerly wind bias over the EIO during maximum warming years. This low level convergence of wind is induced by the bias in the gradient in the mean sea level pressure with positive bias over western EIO and negative bias over EEIO and parts of western Pacific. Consequently, changes in the atmospheric circulation associated with La Niña like conditions affected the ocean dynamics by modulating the current bias thereby enhancing the subsurface warm bias over the EEIO. It is identified that EEIO subsurface warming is stronger when La Niña co-occurred with negative Indian Ocean Dipole events as compared to La Niña only years in the model. Ocean general circulation model (OGCM) experiments forced with CFSv2 winds clearly support our hypothesis that ocean dynamics influenced by westerly winds bias is primarily

The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images.

Science.gov (United States)

Duan, Yuping; Chang, Huibin; Huang, Weimin; Zhou, Jiayin; Lu, Zhongkang; Wu, Chunlin

2015-11-01

We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.

Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models

International Nuclear Information System (INIS)

Liepert, Beate G; Previdi, Michael

2012-01-01

Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are robust results of coupled general circulation models (CGCMs). In spite of these successes, model-to-model variability and biases that are small in first order climate responses, however, have considerable implications for climate predictability especially when multi-model means are used. We show that most climate simulations of the 20th and 21st century A2 scenario performed with CMIP3 (Coupled Model Inter-comparison Project Phase 3) models have deficiencies in simulating the global atmospheric moisture balance. Large biases of only a few models (some biases reach the simulated global precipitation changes in the 20th and 21st centuries) affect the multi-model mean global moisture budget. An imbalanced flux of −0.14 Sv exists while the multi-model median imbalance is only −0.02 Sv. Moreover, for most models the detected imbalance changes over time. As a consequence, in 13 of the 18 CMIP3 models examined, global annual mean precipitation exceeds global evaporation, indicating that there should be a ‘leaking’ of moisture from the atmosphere whereas for the remaining five models a ‘flooding’ is implied. Nonetheless, in all models, the actual atmospheric moisture content and its variability correctly increases during the course of the 20th and 21st centuries. These discrepancies therefore imply an unphysical and hence ‘ghost’ sink/source of atmospheric moisture in the models whose atmospheres flood/leak. The ghost source/sink of moisture can also be regarded as atmospheric latent heating/cooling and hence as positive/negative perturbation of the atmospheric energy budget or non-radiative forcing in the range of −1 to +6 W m −2 (median +0.1 W m −2 ). The inter-model variability of the global atmospheric moisture transport from oceans to land areas, which impacts the terrestrial water cycle, is also quite high and ranges

CD-SEM real time bias correction using reference metrology based modeling

Science.gov (United States)

Ukraintsev, V.; Banke, W.; Zagorodnev, G.; Archie, C.; Rana, N.; Pavlovsky, V.; Smirnov, V.; Briginas, I.; Katnani, A.; Vaid, A.

2018-03-01

Accuracy of patterning impacts yield, IC performance and technology time to market. Accuracy of patterning relies on optical proximity correction (OPC) models built using CD-SEM inputs and intra die critical dimension (CD) control based on CD-SEM. Sub-nanometer measurement uncertainty (MU) of CD-SEM is required for current technologies. Reported design and process related bias variation of CD-SEM is in the range of several nanometers. Reference metrology and numerical modeling are used to correct SEM. Both methods are slow to be used for real time bias correction. We report on real time CD-SEM bias correction using empirical models based on reference metrology (RM) data. Significant amount of currently untapped information (sidewall angle, corner rounding, etc.) is obtainable from SEM waveforms. Using additional RM information provided for specific technology (design rules, materials, processes) CD extraction algorithms can be pre-built and then used in real time for accurate CD extraction from regular CD-SEM images. The art and challenge of SEM modeling is in finding robust correlation between SEM waveform features and bias of CD-SEM as well as in minimizing RM inputs needed to create accurate (within the design and process space) model. The new approach was applied to improve CD-SEM accuracy of 45 nm GATE and 32 nm MET1 OPC 1D models. In both cases MU of the state of the art CD-SEM has been improved by 3x and reduced to a nanometer level. Similar approach can be applied to 2D (end of line, contours, etc.) and 3D (sidewall angle, corner rounding, etc.) cases.

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.

Selection bias, vote counting, and money-priming effects: A comment on Rohrer, Pashler, and Harris (2015) and Vohs (2015).

Science.gov (United States)

Vadillo, Miguel A; Hardwicke, Tom E; Shanks, David R

2016-05-01

When a series of studies fails to replicate a well-documented effect, researchers might be tempted to use a "vote counting" approach to decide whether the effect is reliable-that is, simply comparing the number of successful and unsuccessful replications. Vohs's (2015) response to the absence of money priming effects reported by Rohrer, Pashler, and Harris (2015) provides an example of this approach. Unfortunately, vote counting is a poor strategy to assess the reliability of psychological findings because it neglects the impact of selection bias and questionable research practices. In the present comment, we show that a range of meta-analytic tools indicate irregularities in the money priming literature discussed by Rohrer et al. and Vohs, which all point to the conclusion that these effects are distorted by selection bias, reporting biases, or p-hacking. This could help to explain why money-priming effects have proven unreliable in a number of direct replication attempts in which biases have been minimized through preregistration or transparent reporting. Our major conclusion is that the simple proportion of significant findings is a poor guide to the reliability of research and that preregistered replications are an essential means to assess the reliability of money-priming effects. (c) 2016 APA, all rights reserved).

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.

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.

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

NARCIS (Netherlands)

Bun, M.J.G.

2003-01-01

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J.

Tilted dipole model for bias-dependent photoluminescence pattern

Energy Technology Data Exchange (ETDEWEB)

Fujieda, Ichiro, E-mail: fujieda@se.ritsumei.ac.jp; Suzuki, Daisuke; Masuda, Taishi [Department of Electrical and Electronic Engineering, Ritsumeikan University, Kusatsu 525-8577 (Japan)

2014-12-14

In a guest-host system containing elongated dyes and a nematic liquid crystal, both molecules are aligned to each other. An external bias tilts these molecules and the radiation pattern of the system is altered. A model is proposed to describe this bias-dependent photoluminescence patterns. It divides the liquid crystal/dye layer into sub-layers that contain electric dipoles with specific tilt angles. Each sub-layer emits linearly polarized light. Its radiation pattern is toroidal and is determined by the tilt angle. Its intensity is assumed to be proportional to the power of excitation light absorbed by the sub-layer. This is calculated by the Lambert-Beer's Law. The absorption coefficient is assumed to be proportional to the cross-section of the tilted dipole moment, in analogy to the ellipsoid of refractive index, to evaluate the cross-section for each polarized component of the excitation light. Contributions from all the sub-layers are added to give a final expression for the radiation pattern. Self-absorption is neglected. The model is simplified by reducing the number of sub-layers. Analytical expressions are derived for a simple case that consists of a single layer with tilted dipoles sandwiched by two layers with horizontally-aligned dipoles. All the parameters except for the tilt angle can be determined by measuring transmittance of the excitation light. The model roughly reproduces the bias-dependent photoluminescence patterns of a cell containing 0.5 wt. % coumarin 6. It breaks down at large emission angles. Measured spectral changes suggest that the discrepancy is due to self-absorption and re-emission.

Expectancy bias in a selective conditioning procedure: trait anxiety increases the threat value of a blocked stimulus.

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Boddez, Yannick; Vervliet, Bram; Baeyens, Frank; Lauwers, Stephanie; Hermans, Dirk; Beckers, Tom

2012-06-01

In a blocking procedure, a single conditioned stimulus (CS) is paired with an unconditioned stimulus (US), such as electric shock, in the first stage. During the subsequent stage, the CS is presented together with a second CS and this compound is followed by the same US. Fear conditioning studies in non-human animals have demonstrated that fear responding to the added second CS typically remains low, despite its being paired with the US. Accordingly, the blocking procedure is well suited as a laboratory model for studying (deficits in) selective threat appraisal. The present study tested the relation between trait anxiety and blocking in human aversive conditioning. Healthy participants filled in a trait anxiety questionnaire and underwent blocking treatment in the human aversive conditioning paradigm. Threat appraisal was measured through shock expectancy ratings and skin conductance. As hypothesized, trait anxiety was positively associated with shock expectancy ratings to the blocked stimulus. In skin conductance responding, no significant effects of stimulus type could be detected during blocking training or testing. The current study does not allow strong claims to be made regarding the theoretical process underlying the expectancy bias we observed. The observed shock expectancy bias might be one of the mechanisms leading to non-specific fear in individuals at risk for developing anxiety disorders. A deficit in blocking, or a deficit in selective threat appraisal at the more general level, indeed results in fear becoming non-specific and disconnected from the most likely causes or predictors of danger. Copyright © 2011 Elsevier Ltd. All rights reserved.

NUMERICAL MODELLING AND EXPERIMENTAL INFLATION VALIDATION OF A BIAS TWO-WHEEL TIRE

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CHUNG KET THEIN

2016-02-01

Full Text Available This paper presents a parametric study on the development of a computational model for bias two-wheel tire through finite element analysis (FEA. An 80/90- 17 bias two-wheel tire was adopted which made up of four major layers of rubber compound with different material properties to strengthen the structure. Mooney-Rivlin hyperelastic model was applied to represent the behaviour of incompressible rubber compound. A 3D tire model was built for structural static finite element analysis. The result was validated from the inflation analysis. Structural static finite element analysis method is suitable for evaluation of the tire design and improvement of the tire behaviour to desired performance. Experimental tire was inflated at various pressures and the geometry between numerical and experimental tire were compared. There are good agreements between numerical simulation model and the experiment results. This indicates that the simulation model can be applied to the bias two-wheel tire design in order to predict the tire behaviour and improve its mechanical characteristics.

Assessing the utility of frequency dependent nudging for reducing biases in biogeochemical models

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Lagman, Karl B.; Fennel, Katja; Thompson, Keith R.; Bianucci, Laura

2014-09-01

Bias errors, resulting from inaccurate boundary and forcing conditions, incorrect model parameterization, etc. are a common problem in environmental models including biogeochemical ocean models. While it is important to correct bias errors wherever possible, it is unlikely that any environmental model will ever be entirely free of such errors. Hence, methods for bias reduction are necessary. A widely used technique for online bias reduction is nudging, where simulated fields are continuously forced toward observations or a climatology. Nudging is robust and easy to implement, but suppresses high-frequency variability and introduces artificial phase shifts. As a solution to this problem Thompson et al. (2006) introduced frequency dependent nudging where nudging occurs only in prescribed frequency bands, typically centered on the mean and the annual cycle. They showed this method to be effective for eddy resolving ocean circulation models. Here we add a stability term to the previous form of frequency dependent nudging which makes the method more robust for non-linear biological models. Then we assess the utility of frequency dependent nudging for biological models by first applying the method to a simple predator-prey model and then to a 1D ocean biogeochemical model. In both cases we only nudge in two frequency bands centered on the mean and the annual cycle, and then assess how well the variability in higher frequency bands is recovered. We evaluate the effectiveness of frequency dependent nudging in comparison to conventional nudging and find significant improvements with the former.

Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions

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Asma Foughali

2015-07-01

Full Text Available This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km2 in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960–1971 is selected for calibration while the period 1972–1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT from the European program ENSEMBLES, forced by two global climate models (GCM: ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM precipitation must be corrected before being introduced as MBBH inputs. Thus, a non-parametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to

Perceptual Color Space Representations in the Oculomotor System Are Modulated by Surround Suppression and Biased Selection

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Devin H. Kehoe

2018-01-01

Full Text Available The oculomotor system utilizes color extensively for planning saccades. Therefore, we examined how the oculomotor system actually encodes color and several factors that modulate these representations: attention-based surround suppression and inherent biases in selecting and encoding color categories. We measured saccade trajectories while human participants performed a memory-guided saccade task with color targets and distractors and examined whether oculomotor target selection processing was functionally related to the CIE (x,y color space distances between color stimuli and whether there were hierarchical differences between color categories in the strength and speed of encoding potential saccade goals. We observed that saccade planning was modulated by the CIE (x,y distances between stimuli thus demonstrating that color is encoded in perceptual color space by the oculomotor system. Furthermore, these representations were modulated by (1 cueing attention to a particular color thereby eliciting surround suppression in oculomotor color space and (2 inherent selection and encoding biases based on color category independent of cueing and perceptual discriminability. Since surround suppression emerges from recurrent feedback attenuation of sensory projections, observing oculomotor surround suppression suggested that oculomotor encoding of behavioral relevance results from integrating sensory and cognitive signals that are pre-attenuated based on task demands and that the oculomotor system therefore does not functionally contribute to this process. Second, although perceptual discriminability did partially account for oculomotor processing differences between color categories, we also observed preferential processing of the red color category across various behavioral metrics. This is consistent with numerous previous studies and could not be simply explained by perceptual discriminability. Since we utilized a memory-guided saccade task, this

Perceptual Color Space Representations in the Oculomotor System Are Modulated by Surround Suppression and Biased Selection.

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Kehoe, Devin H; Rahimi, Maryam; Fallah, Mazyar

2018-01-01

The oculomotor system utilizes color extensively for planning saccades. Therefore, we examined how the oculomotor system actually encodes color and several factors that modulate these representations: attention-based surround suppression and inherent biases in selecting and encoding color categories. We measured saccade trajectories while human participants performed a memory-guided saccade task with color targets and distractors and examined whether oculomotor target selection processing was functionally related to the CIE ( x , y ) color space distances between color stimuli and whether there were hierarchical differences between color categories in the strength and speed of encoding potential saccade goals. We observed that saccade planning was modulated by the CIE ( x , y ) distances between stimuli thus demonstrating that color is encoded in perceptual color space by the oculomotor system. Furthermore, these representations were modulated by (1) cueing attention to a particular color thereby eliciting surround suppression in oculomotor color space and (2) inherent selection and encoding biases based on color category independent of cueing and perceptual discriminability. Since surround suppression emerges from recurrent feedback attenuation of sensory projections, observing oculomotor surround suppression suggested that oculomotor encoding of behavioral relevance results from integrating sensory and cognitive signals that are pre-attenuated based on task demands and that the oculomotor system therefore does not functionally contribute to this process. Second, although perceptual discriminability did partially account for oculomotor processing differences between color categories, we also observed preferential processing of the red color category across various behavioral metrics. This is consistent with numerous previous studies and could not be simply explained by perceptual discriminability. Since we utilized a memory-guided saccade task, this indicates that

Bias correction in the realized stochastic volatility model for daily volatility on the Tokyo Stock Exchange

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Takaishi, Tetsuya

2018-06-01

The realized stochastic volatility model has been introduced to estimate more accurate volatility by using both daily returns and realized volatility. The main advantage of the model is that no special bias-correction factor for the realized volatility is required a priori. Instead, the model introduces a bias-correction parameter responsible for the bias hidden in realized volatility. We empirically investigate the bias-correction parameter for realized volatilities calculated at various sampling frequencies for six stocks on the Tokyo Stock Exchange, and then show that the dynamic behavior of the bias-correction parameter as a function of sampling frequency is qualitatively similar to that of the Hansen-Lunde bias-correction factor although their values are substantially different. Under the stochastic diffusion assumption of the return dynamics, we investigate the accuracy of estimated volatilities by examining the standardized returns. We find that while the moments of the standardized returns from low-frequency realized volatilities are consistent with the expectation from the Gaussian variables, the deviation from the expectation becomes considerably large at high frequencies. This indicates that the realized stochastic volatility model itself cannot completely remove bias at high frequencies.

Selection Bias in Students' Evaluation of Teaching: Causes of Student Absenteeism and Its Consequences for Course Ratings and Rankings

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Wolbring, Tobias; Treischl, Edgar

2016-01-01

Systematic sampling error due to self-selection is a common topic in methodological research and a key challenge for every empirical study. Since selection bias is often not sufficiently considered as a potential flaw in research on and evaluations in higher education, the aim of this paper is to raise awareness for the topic using the case of…

Duration on unemployment: geographic mobility and selectivity bias.

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Goss, E P; Paul, C; Wilhite, A

1994-01-01

Modeling the factors affecting the duration of unemployment was found to be influenced by the inclusion of migration factors. Traditional models which did not control for migration factors were found to underestimate movers' probability of finding an acceptable job. The empirical test of the theory, based on the analysis of data on US household heads unemployed in 1982 and employed in 1982 and 1983, found that the cumulative probability of reemployment in the traditional model was .422 and in the migration selectivity model was .624 after 30 weeks of searching. In addition, controlling for selectivity eliminated the significance of the relationship between race and job search duration in the model. The relationship between search duration and the county unemployment rate in 1982 became statistically significant, and the relationship between search duration and 1980 population per square mile in the 1982 county of residence became statistically insignificant. The finding that non-Whites have a longer duration of unemployment can better be understood as non-Whites' lower geographic mobility and lack of greater job contacts. The statistical significance of a high unemployment rate in the home labor market reducing the probability of finding employment was more in keeping with expectations. The findings assumed that the duration of employment accurately reflected the length of job search. The sample was redrawn to exclude discouraged workers and the analysis was repeated. The findings were similar to the full sample, with the coefficient for migration variable being negative and statistically significant and the coefficient for alpha remaining positive and statistically significant. Race in the selectivity model remained statistically insignificant. The findings supported the Schwartz model hypothesizing that the expansion of the radius of the search would reduce the duration of unemployment. The exclusion of the migration factor misspecified the equation for

Estimating the price elasticity of beer: meta-analysis of data with heterogeneity, dependence, and publication bias.

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Nelson, Jon P

2014-01-01

Precise estimates of price elasticities are important for alcohol tax policy. Using meta-analysis, this paper corrects average beer elasticities for heterogeneity, dependence, and publication selection bias. A sample of 191 estimates is obtained from 114 primary studies. Simple and weighted means are reported. Dependence is addressed by restricting number of estimates per study, author-restricted samples, and author-specific variables. Publication bias is addressed using funnel graph, trim-and-fill, and Egger's intercept model. Heterogeneity and selection bias are examined jointly in meta-regressions containing moderator variables for econometric methodology, primary data, and precision of estimates. Results for fixed- and random-effects regressions are reported. Country-specific effects and sample time periods are unimportant, but several methodology variables help explain the dispersion of estimates. In models that correct for selection bias and heterogeneity, the average beer price elasticity is about -0.20, which is less elastic by 50% compared to values commonly used in alcohol tax policy simulations. Copyright © 2013 Elsevier B.V. All rights reserved.

A selective emotional decision-making bias elicited by facial expressions.

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Nicholas Furl

Full Text Available Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices.

A Selective Emotional Decision-Making Bias Elicited by Facial Expressions

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Furl, Nicholas; Gallagher, Shannon; Averbeck, Bruno B.

2012-01-01

Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices. PMID:22438936

A selective emotional decision-making bias elicited by facial expressions.

Science.gov (United States)

Furl, Nicholas; Gallagher, Shannon; Averbeck, Bruno B

2012-01-01

Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices.

The (neurocognitive mechanisms behind attention bias modification in anxiety: Proposals based on theoretical accounts of attentional bias

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Alexandre eHeeren

2013-04-01

Full Text Available Recently, researchers have investigated the causal nature of attentional bias for threat (AB in the maintenance of anxiety disorders by experimentally manipulating it. They found that training anxious individuals to attend to nonthreat stimuli reduces AB, which, in turn, reduces anxiety. This effect supports the hypothesis that AB can causally impact the maintenance of anxiety. At a fundamental level, however, uncertainty still abounds regarding the nature of the processes that mediate this effect. In the present paper, we propose that two contrasting approaches may be derived from theoretical accounts of AB. According to a first class of models, called the valence-specific bias models, modifying AB requires the modification of valence-specific attentional selectivity. According to a second class of models, called the attention control models, modifying AB requires the modification of attention control, driven by the recruitment of the dorsolateral prefrontal cortex. We formulate a series of specific predictions, to provide suggestions to trial these two approaches one against the other. This knowledge is critical for understanding the mechanisms of AB in anxiety disorders, which bares important clinical implications.

Non-stationary Bias Correction of Monthly CMIP5 Temperature Projections over China using a Residual-based Bagging Tree Model

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Yang, T.; Lee, C.

2017-12-01

The biases in the Global Circulation Models (GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed Residual-based Bagging Tree (RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modeling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared to the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44 ºC, 2.59 ºC, and 4.71 ºC by the end of the century, respectively, when compared to the average temperature during 1970 - 1999.

Climate model biases in seasonality of continental water storage revealed by satellite gravimetry

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Swenson, Sean; Milly, P.C.D.

2006-01-01

Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low‐latitude, hemispheric storage generally occur too early in the models, and model‐specific errors in amplitude of the low‐latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models.

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

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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.

Sympathetic bias.

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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.

Bias Correction in a Stable AD (1,1) Model: Weak versus Strong Exogeneity

NARCIS (Netherlands)

van Giersbergen, N.P.A.

2001-01-01

This paper compares the behaviour of a bias-corrected estimator assuming strongly exogenous regressors to the behaviour of a bias-corrected estimator assuming weakly exogenous regressors, when in fact the marginal model contains a feedback mechanism. To this end, the effects of a feedback mechanism

SPICE Modeling of Body Bias Effect in 4H-SiC Integrated Circuit Resistors

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Neudeck, Philip G.

2017-01-01

The DC electrical behavior of n-type 4H-SiC resistors used for realizing 500C durable integrated circuits (ICs) is studied as a function of substrate bias and temperature. Improved fidelity electrical simulation is described using SPICE NMOS model to simulate resistor substrate body bias effect that is absent from the SPICE semiconductor resistor model.

Is there room for 'development' in developmental models of information processing biases to threat in children and adolescents?

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Field, Andy P; Lester, Kathryn J

2010-12-01

Clinical and experimental theories assume that processing biases in attention and interpretation are a causal mechanism through which anxiety develops. Despite growing evidence that these processing biases are present in children and, therefore, develop long before adulthood, these theories ignore the potential role of child development. This review attempts to place information processing biases within a theoretical developmental framework. We consider whether child development has no impact on information processing biases to threat (integral bias model), or whether child development influences information processing biases and if so whether it does so by moderating the expression of an existing bias (moderation model) or by affecting the acquisition of a bias (acquisition model). We examine the extent to which these models fit with existing theory and research evidence and outline some methodological issues that need to be considered when drawing conclusions about the potential role of child development in the information processing of threat stimuli. Finally, we speculate about the developmental processes that might be important to consider in future research.

A bayesian hierarchical model for classification with selection of functional predictors.

Science.gov (United States)

Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D

2010-06-01

In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.

Model of divertor biasing and control of scrape-off layer and divertor plasmas

International Nuclear Information System (INIS)

Nagasaki, K.; Itoh, K.; Itoh, S.

1991-02-01

Analytic model of the divertor biasing is described. For the given plasma and energy sources from the core plasma, the heat and particle flux densities on the divertor plate as well as scrape-off-layer (SOL)/divertor plasmas are analyzed in a slab model. Using a two-dimensional model, the effects of the divertor biasing and SOL current are studied. The conditions to balance the plasma temperature or sheath potential on different divertor plates are obtained. Effect of the SOL current on the heat channel width is also discussed. (author)

Mental health courts and their selection processes: modeling variation for consistency.

Science.gov (United States)

Wolff, Nancy; Fabrikant, Nicole; Belenko, Steven

2011-10-01

Admission into mental health courts is based on a complicated and often variable decision-making process that involves multiple parties representing different expertise and interests. To the extent that eligibility criteria of mental health courts are more suggestive than deterministic, selection bias can be expected. Very little research has focused on the selection processes underpinning problem-solving courts even though such processes may dominate the performance of these interventions. This article describes a qualitative study designed to deconstruct the selection and admission processes of mental health courts. In this article, we describe a multi-stage, complex process for screening and admitting clients into mental health courts. The selection filtering model that is described has three eligibility screening stages: initial, assessment, and evaluation. The results of this study suggest that clients selected by mental health courts are shaped by the formal and informal selection criteria, as well as by the local treatment system.

Accounting for animal movement in estimation of resource selection functions: sampling and data analysis.

Science.gov (United States)

Forester, James D; Im, Hae Kyung; Rathouz, Paul J

2009-12-01

Patterns of resource selection by animal populations emerge as a result of the behavior of many individuals. Statistical models that describe these population-level patterns of habitat use can miss important interactions between individual animals and characteristics of their local environment; however, identifying these interactions is difficult. One approach to this problem is to incorporate models of individual movement into resource selection models. To do this, we propose a model for step selection functions (SSF) that is composed of a resource-independent movement kernel and a resource selection function (RSF). We show that standard case-control logistic regression may be used to fit the SSF; however, the sampling scheme used to generate control points (i.e., the definition of availability) must be accommodated. We used three sampling schemes to analyze simulated movement data and found that ignoring sampling and the resource-independent movement kernel yielded biased estimates of selection. The level of bias depended on the method used to generate control locations, the strength of selection, and the spatial scale of the resource map. Using empirical or parametric methods to sample control locations produced biased estimates under stronger selection; however, we show that the addition of a distance function to the analysis substantially reduced that bias. Assuming a uniform availability within a fixed buffer yielded strongly biased selection estimates that could be corrected by including the distance function but remained inefficient relative to the empirical and parametric sampling methods. As a case study, we used location data collected from elk in Yellowstone National Park, USA, to show that selection and bias may be temporally variable. Because under constant selection the amount of bias depends on the scale at which a resource is distributed in the landscape, we suggest that distance always be included as a covariate in SSF analyses. This approach to

Detection of bias in animal model pedigree indices of heifers

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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.;

Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

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Duarte, Adam; Adams, Michael J.; Peterson, James T.

2018-01-01

Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision

Educational Research with Real-World Data: Reducing Selection Bias with Propensity Scores

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Jill L. Adelson

2013-12-01

Full Text Available Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations of treatment effects is propensity score analysis. This method reduces a large number of pretreatment covariates to a single scalar function and allows researchers to compare subjects with similar probability to receive the treatment. This article provides an introduction to propensity score analysis and stratification, an example illustrating its use, and suggestions for using propensity score analysis in educational research.

Sensitivity analysis for publication bias in meta-analysis of diagnostic studies for a continuous biomarker.

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Hattori, Satoshi; Zhou, Xiao-Hua

2018-02-10

Publication bias is one of the most important issues in meta-analysis. For standard meta-analyses to examine intervention effects, the funnel plot and the trim-and-fill method are simple and widely used techniques for assessing and adjusting for the influence of publication bias, respectively. However, their use may be subjective and can then produce misleading insights. To make a more objective inference for publication bias, various sensitivity analysis methods have been proposed, including the Copas selection model. For meta-analysis of diagnostic studies evaluating a continuous biomarker, the summary receiver operating characteristic (sROC) curve is a very useful method in the presence of heterogeneous cutoff values. To our best knowledge, no methods are available for evaluation of influence of publication bias on estimation of the sROC curve. In this paper, we introduce a Copas-type selection model for meta-analysis of diagnostic studies and propose a sensitivity analysis method for publication bias. Our method enables us to assess the influence of publication bias on the estimation of the sROC curve and then judge whether the result of the meta-analysis is sufficiently confident or should be interpreted with much caution. We illustrate our proposed method with real data. Copyright © 2017 John Wiley & Sons, Ltd.

A single extracellular amino acid in Free Fatty Acid Receptor 2 defines antagonist species selectivity and G protein selection bias

DEFF Research Database (Denmark)

Sergeev, Eugenia; Hansen, Anders Højgaard; Bolognini, Daniele

2017-01-01

selectivity and mutational swap studies confirmed this hypothesis. Extending these studies to agonist function indicated that although the lysine - arginine variation between human and mouse orthologs had limited effect on G protein-mediated signal transduction, removal of positive charge from this residue...... produced a signalling-biased variant of Free Fatty Acid Receptor 2 in which Gi-mediated signalling by both short chain fatty acids and synthetic agonists was maintained whilst there was marked loss of agonist potency for signalling via Gq/11 and G12/13 G proteins. A single residue at the extracellular face...

Inviting parents to take part in paediatric palliative care research: a mixed-methods examination of selection bias.

Science.gov (United States)

Crocker, Joanna C; Beecham, Emma; Kelly, Paula; Dinsdale, Andrew P; Hemsley, June; Jones, Louise; Bluebond-Langner, Myra

2015-03-01

Recruitment to paediatric palliative care research is challenging, with high rates of non-invitation of eligible families by clinicians. The impact on sample characteristics is unknown. To investigate, using mixed methods, non-invitation of eligible families and ensuing selection bias in an interview study about parents' experiences of advance care planning (ACP). We examined differences between eligible families invited and not invited to participate by clinicians using (1) field notes of discussions with clinicians during the invitation phase and (2) anonymised information from the service's clinical database. Families were eligible for the ACP study if their child was receiving care from a UK-based tertiary palliative care service (Group A; N = 519) or had died 6-10 months previously having received care from the service (Group B; N = 73). Rates of non-invitation to the ACP study were high. A total of 28 (5.4%) Group A families and 21 (28.8%) Group B families (p research findings. Non-invitation and selection bias should be considered, assessed and reported in palliative care studies. © The Author(s) 2014.

Bias correction in species distribution models: pooling survey and collection data for multiple species.

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Fithian, William; Elith, Jane; Hastie, Trevor; Keith, David A

2015-04-01

Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence-absence or count data collected in systematic, planned surveys are more reliable but typically less abundant.We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence-absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting for the sampling bias affecting the presence-only data. By assuming that the sampling bias is the same for all species, we can borrow strength across species to efficiently estimate the bias and improve our inference from presence-only data.We evaluate our model's performance on data for 36 eucalypt species in south-eastern Australia. We find that presence-only records exhibit a strong sampling bias towards the coast and towards Sydney, the largest city. Our data-pooling technique substantially improves the out-of-sample predictive performance of our model when the amount of available presence-absence data for a given species is scarceIf we have only presence-only data and no presence-absence data for a given species, but both types of data for several other species that suffer from the same spatial sampling bias, then our method can obtain an unbiased estimate of the first species' geographic range.

Proportional hazards model with varying coefficients for length-biased data.

Science.gov (United States)

Zhang, Feipeng; Chen, Xuerong; Zhou, Yong

2014-01-01

Length-biased data arise in many important applications including epidemiological cohort studies, cancer prevention trials and studies of labor economics. Such data are also often subject to right censoring due to loss of follow-up or the end of study. In this paper, we consider a proportional hazards model with varying coefficients for right-censored and length-biased data, which is used to study the interact effect nonlinearly of covariates with an exposure variable. A local estimating equation method is proposed for the unknown coefficients and the intercept function in the model. The asymptotic properties of the proposed estimators are established by using the martingale theory and kernel smoothing techniques. Our simulation studies demonstrate that the proposed estimators have an excellent finite-sample performance. The Channing House data is analyzed to demonstrate the applications of the proposed method.

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....

Approach-Induced Biases in Human Information Sampling.

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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.

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

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Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

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Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

Selective processing of smoking-related cues in smokers: manipulation of deprivation level and comparison of three measures of processing bias.

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Mogg, Karin; Bradley, Brendan P

2002-12-01

Recent theories of addiction suggest that an attentional bias for drug-related cues plays an important role in maintaining drug-taking behaviours. A key feature of the present study is that it used three different measures of processing bias for linguistic and pictorial smoking-related cues: masked and unmasked conditions of the modified Stroop task, and a pictorial version of the visual probe task. Participants were smokers (n = 27), who were tested twice, with deprivation level manipulated as a within-subjects variable. They were asked to abstain from smoking for 12 h before one session, and to smoke normally before the other. Results were consistent with an attentional bias for smoking-related pictures on the visual probe task, and for smoking-related words in the unmasked condition of the modified Stroop task. The latter bias was most strongly predicted by self-reported urge to smoke, rather than by the deprivation manipulation. There was no evidence of a preconscious bias for smoking cues. The three measures of cognitive bias (from masked and unmasked Stroop and visual probe tasks) were not significantly correlated with each other, which suggests they may tap different underlying mechanisms. We discuss the results with respect to conceptualizations of selective attention, addiction and motivational states in general.

Structural optimization and structure-functional selectivity relationship studies of G protein-biased EP2 receptor agonists.

Science.gov (United States)

Ogawa, Seiji; Watanabe, Toshihide; Moriyuki, Kazumi; Goto, Yoshikazu; Yamane, Shinsaku; Watanabe, Akio; Tsuboi, Kazuma; Kinoshita, Atsushi; Okada, Takuya; Takeda, Hiroyuki; Tani, Kousuke; Maruyama, Toru

2016-05-15

The modification of the novel G protein-biased EP2 agonist 1 has been investigated to improve its G protein activity and develop a better understanding of its structure-functional selectivity relationship (SFSR). The optimization of the substituents on the phenyl ring of 1, followed by the inversion of the hydroxyl group on the cyclopentane moiety led to compound 9, which showed a 100-fold increase in its G protein activity compared with 1 without any increase in β-arrestin recruitment. Furthermore, SFSR studies revealed that the combination of meta and para substituents on the phenyl moiety was crucial to the functional selectivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

The impact of stroke unit care on outcome in a Scottish stroke population, taking into account case mix and selection bias.

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Turner, Melanie; Barber, Mark; Dodds, Hazel; Dennis, Martin; Langhorne, Peter; Macleod, Mary Joan

2015-03-01

Randomised trials indicate that stroke unit care reduces morbidity and mortality after stroke. Similar results have been seen in observational studies but many have not corrected for selection bias or independent predictors of outcome. We evaluated the effect of stroke unit compared with general ward care on outcomes after stroke in Scotland, adjusting for case mix by incorporating the six simple variables (SSV) model, also taking into account selection bias and stroke subtype. We used routine data from National Scottish datasets for acute stroke patients admitted between 2005 and 2011. Patients who died within 3 days of admission were excluded from analysis. The main outcome measures were survival and discharge home. Multivariable logistic regression was used to estimate the OR for survival, and adjustment was made for the effect of the SSV model and for early mortality. Cox proportional hazards model was used to estimate the hazard of death within 365 days. There were 41 692 index stroke events; 79% were admitted to a stroke unit at some point during their hospital stay and 21% were cared for in a general ward. Using the SSV model, we obtained a receiver operated curve of 0.82 (SE 0.002) for mortality at 6 months. The adjusted OR for survival at 7 days was 3.11 (95% CI 2.71 to 3.56) and at 1 year 1.43 (95% CI 1.34 to 1.54) while the adjusted OR for being discharged home was 1.19 (95% CI 1.11 to 1.28) for stroke unit care. In routine practice, stroke unit admission is associated with a greater likelihood of discharge home and with lower mortality up to 1 year, after correcting for known independent predictors of outcome, and excluding early non-modifiable mortality. 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.

Same-strand overlapping genes in bacteria: compositional determinants of phase bias

Directory of Open Access Journals (Sweden)

Landan Giddy

2008-08-01

Full Text Available Abstract Background Same-strand overlapping genes may occur in frameshifts of one (phase 1 or two nucleotides (phase 2. In previous studies of bacterial genomes, long phase-1 overlaps were found to be more numerous than long phase-2 overlaps. This bias was explained by either genomic location or an unspecified selection advantage. Models that focused on the ability of the two genes to evolve independently did not predict this phase bias. Here, we propose that a purely compositional model explains the phase bias in a more parsimonious manner. Same-strand overlapping genes may arise through either a mutation at the termination codon of the upstream gene or a mutation at the initiation codon of the downstream gene. We hypothesized that given these two scenarios, the frequencies of initiation and termination codons in the two phases may determine the number for overlapping genes. Results We examined the frequencies of initiation- and termination-codons in the two phases, and found that termination codons do not significantly differ between the two phases, whereas initiation codons are more abundant in phase 1. We found that the primary factors explaining the phase inequality are the frequencies of amino acids whose codons may combine to form start codons in the two phases. We show that the frequencies of start codons in each of the two phases, and, hence, the potential for the creation of overlapping genes, are determined by a universal amino-acid frequency and species-specific codon usage, leading to a correlation between long phase-1 overlaps and genomic GC content. Conclusion Our model explains the phase bias in same-strand overlapping genes by compositional factors without invoking selection. Therefore, it can be used as a null model of neutral evolution to test selection hypotheses concerning the evolution of overlapping genes. Reviewers This article was reviewed by Bill Martin, Itai Yanai, and Mikhail Gelfand.

Producing physically consistent and bias free extreme precipitation events over the Switzerland: Bridging gaps between meteorology and impact models

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José Gómez-Navarro, Juan; Raible, Christoph C.; Blumer, Sandro; Martius, Olivia; Felder, Guido

2016-04-01

Extreme precipitation episodes, although rare, are natural phenomena that can threat human activities, especially in areas densely populated such as Switzerland. Their relevance demands the design of public policies that protect public assets and private property. Therefore, increasing the current understanding of such exceptional situations is required, i.e. the climatic characterisation of their triggering circumstances, severity, frequency, and spatial distribution. Such increased knowledge shall eventually lead us to produce more reliable projections about the behaviour of these events under ongoing climate change. Unfortunately, the study of extreme situations is hampered by the short instrumental record, which precludes a proper characterization of events with return period exceeding few decades. This study proposes a new approach that allows studying storms based on a synthetic, but physically consistent database of weather situations obtained from a long climate simulation. Our starting point is a 500-yr control simulation carried out with the Community Earth System Model (CESM). In a second step, this dataset is dynamically downscaled with the Weather Research and Forecasting model (WRF) to a final resolution of 2 km over the Alpine area. However, downscaling the full CESM simulation at such high resolution is infeasible nowadays. Hence, a number of case studies are previously selected. This selection is carried out examining the precipitation averaged in an area encompassing Switzerland in the ESM. Using a hydrological criterion, precipitation is accumulated in several temporal windows: 1 day, 2 days, 3 days, 5 days and 10 days. The 4 most extreme events in each category and season are selected, leading to a total of 336 days to be simulated. The simulated events are affected by systematic biases that have to be accounted before this data set can be used as input in hydrological models. Thus, quantile mapping is used to remove such biases. For this task

Collective properties of injection-induced earthquake sequences: 1. Model description and directivity bias

Science.gov (United States)

Dempsey, David; Suckale, Jenny

2016-05-01

Induced seismicity is of increasing concern for oil and gas, geothermal, and carbon sequestration operations, with several M > 5 events triggered in recent years. Modeling plays an important role in understanding the causes of this seismicity and in constraining seismic hazard. Here we study the collective properties of induced earthquake sequences and the physics underpinning them. In this first paper of a two-part series, we focus on the directivity ratio, which quantifies whether fault rupture is dominated by one (unilateral) or two (bilateral) propagating fronts. In a second paper, we focus on the spatiotemporal and magnitude-frequency distributions of induced seismicity. We develop a model that couples a fracture mechanics description of 1-D fault rupture with fractal stress heterogeneity and the evolving pore pressure distribution around an injection well that triggers earthquakes. The extent of fault rupture is calculated from the equations of motion for two tips of an expanding crack centered at the earthquake hypocenter. Under tectonic loading conditions, our model exhibits a preference for unilateral rupture and a normal distribution of hypocenter locations, two features that are consistent with seismological observations. On the other hand, catalogs of induced events when injection occurs directly onto a fault exhibit a bias toward ruptures that propagate toward the injection well. This bias is due to relatively favorable conditions for rupture that exist within the high-pressure plume. The strength of the directivity bias depends on a number of factors including the style of pressure buildup, the proximity of the fault to failure and event magnitude. For injection off a fault that triggers earthquakes, the modeled directivity bias is small and may be too weak for practical detection. For two hypothetical injection scenarios, we estimate the number of earthquake observations required to detect directivity bias.

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.

Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for climate change in Central Europe

Directory of Open Access Journals (Sweden)

A. P. van Ulden

2006-01-01

Full Text Available The quality of global sea level pressure patterns has been assessed for simulations by 23 coupled climate models. Most models showed high pattern correlations. With respect to the explained spatial variance, many models showed serious large-scale deficiencies, especially at mid-latitudes. Five models performed well at all latitudes and for each month of the year. Three models had a reasonable skill. We selected the five models with the best pressure patterns for a more detailed assessment of their simulations of the climate in Central Europe. We analysed observations and simulations of monthly mean geostrophic flow indices and of monthly mean temperature and precipitation. We used three geostrophic flow indices: the west component and south component of the geostrophic wind at the surface and the geostrophic vorticity. We found that circulation biases were important, and affected precipitation in particular. Apart from these circulation biases, the models showed other biases in temperature and precipitation, which were for some models larger than the circulation induced biases. For the 21st century the five models simulated quite different changes in circulation, precipitation and temperature. Precipitation changes appear to be primarily caused by circulation changes. Since the models show widely different circulation changes, especially in late summer, precipitation changes vary widely between the models as well. Some models simulate severe drying in late summer, while one model simulates significant precipitation increases in late summer. With respect to the mean temperature the circulation changes were important, but not dominant. However, changes in the distribution of monthly mean temperatures, do show large indirect influences of circulation changes. Especially in late summer, two models simulate very strong warming of warm months, which can be attributed to severe summer drying in the simulations by these models. The models differ also

Detecting and correcting for publication bias in meta-analysis - A truncated normal distribution approach.

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Zhu, Qiaohao; Carriere, K C

2016-01-01

Publication bias can significantly limit the validity of meta-analysis when trying to draw conclusion about a research question from independent studies. Most research on detection and correction for publication bias in meta-analysis focus mainly on funnel plot-based methodologies or selection models. In this paper, we formulate publication bias as a truncated distribution problem, and propose new parametric solutions. We develop methodologies of estimating the underlying overall effect size and the severity of publication bias. We distinguish the two major situations, in which publication bias may be induced by: (1) small effect size or (2) large p-value. We consider both fixed and random effects models, and derive estimators for the overall mean and the truncation proportion. These estimators will be obtained using maximum likelihood estimation and method of moments under fixed- and random-effects models, respectively. We carried out extensive simulation studies to evaluate the performance of our methodology, and to compare with the non-parametric Trim and Fill method based on funnel plot. We find that our methods based on truncated normal distribution perform consistently well, both in detecting and correcting publication bias under various situations.

Causes of the large warm bias in the Angola-Benguela Frontal Zone in the Norwegian Earth System Model

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Koseki, Shunya; Keenlyside, Noel; Demissie, Teferi; Toniazzo, Thomas; Counillon, Francois; Bethke, Ingo; Ilicak, Mehmet; Shen, Mao-Lin

2018-06-01

We have investigated the causes of the sea surface temperature (SST) bias in the Angola-Benguela Frontal Zone (ABFZ) of the southeastern Atlantic Ocean simulated by the Norwegian Earth System Model (NorESM). Similar to other coupled-models, NorESM has a warm SST bias in the ABFZ of up to 8 °C in the annual mean. Our analysis of NorESM reveals that a cyclonic surface wind bias over the ABFZ drives a locally excessively strong southward (0.05 m/s (relative to observation)) Angola Current displacing the ABFZ southward. A series of uncoupled stand-alone atmosphere and ocean model simulations are performed to investigate the cause of the coupled model bias. The stand-alone atmosphere model driven with observed SST exhibits a similar cyclonic surface circulation bias; while the stand-alone ocean model forced with the reanalysis data produces a warm SST in the ABFZ with a magnitude approximately half of that in the coupled NorESM simulation. An additional uncoupled sensitivity experiment shows that the atmospheric model's local negative surface wind curl generates anomalously strong Angola Current at the ocean surface. Consequently, this contributes to the warm SST bias in the ABFZ by 2 °C (compared to the reanalysis forced simulation). There is no evidence that local air-sea feedbacks among wind stress curl, SST, and sea level pressure (SLP) affect the ABFZ SST bias. Turbulent surface heat flux differences between coupled and uncoupled experiments explain the remaining 2 °C warm SST bias in NorESM. Ocean circulation, upwelling and turbulent heat flux errors all modulate the intensity and the seasonality of the ABFZ errors.

Causes of the large warm bias in the Angola-Benguela Frontal Zone in the Norwegian Earth System Model

Science.gov (United States)

Koseki, Shunya; Keenlyside, Noel; Demissie, Teferi; Toniazzo, Thomas; Counillon, Francois; Bethke, Ingo; Ilicak, Mehmet; Shen, Mao-Lin

2017-09-01

We have investigated the causes of the sea surface temperature (SST) bias in the Angola-Benguela Frontal Zone (ABFZ) of the southeastern Atlantic Ocean simulated by the Norwegian Earth System Model (NorESM). Similar to other coupled-models, NorESM has a warm SST bias in the ABFZ of up to 8 °C in the annual mean. Our analysis of NorESM reveals that a cyclonic surface wind bias over the ABFZ drives a locally excessively strong southward (0.05 m/s (relative to observation)) Angola Current displacing the ABFZ southward. A series of uncoupled stand-alone atmosphere and ocean model simulations are performed to investigate the cause of the coupled model bias. The stand-alone atmosphere model driven with observed SST exhibits a similar cyclonic surface circulation bias; while the stand-alone ocean model forced with the reanalysis data produces a warm SST in the ABFZ with a magnitude approximately half of that in the coupled NorESM simulation. An additional uncoupled sensitivity experiment shows that the atmospheric model's local negative surface wind curl generates anomalously strong Angola Current at the ocean surface. Consequently, this contributes to the warm SST bias in the ABFZ by 2 °C (compared to the reanalysis forced simulation). There is no evidence that local air-sea feedbacks among wind stress curl, SST, and sea level pressure (SLP) affect the ABFZ SST bias. Turbulent surface heat flux differences between coupled and uncoupled experiments explain the remaining 2 °C warm SST bias in NorESM. Ocean circulation, upwelling and turbulent heat flux errors all modulate the intensity and the seasonality of the ABFZ errors.

Selection of Representative Models for Decision Analysis Under Uncertainty

Science.gov (United States)

Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

2016-03-01

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

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.

Pareto genealogies arising from a Poisson branching evolution model with selection.

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Huillet, Thierry E

2014-02-01

We study a class of coalescents derived from a sampling procedure out of N i.i.d. Pareto(α) random variables, normalized by their sum, including β-size-biasing on total length effects (β Poisson-Dirichlet (α, -β) Ξ-coalescent (α ε[0, 1)), or to a family of continuous-time Beta (2 - α, α - β)Λ-coalescents (α ε[1, 2)), or to the Kingman coalescent (α ≥ 2). We indicate that this class of coalescent processes (and their scaling limits) may be viewed as the genealogical processes of some forward in time evolving branching population models including selection effects. In such constant-size population models, the reproduction step, which is based on a fitness-dependent Poisson Point Process with scaling power-law(α) intensity, is coupled to a selection step consisting of sorting out the N fittest individuals issued from the reproduction step.

[Potential selection bias in telephone surveys: landline and mobile phones].

Science.gov (United States)

Garcia-Continente, Xavier; Pérez-Giménez, Anna; López, María José; Nebot, Manel

2014-01-01

The increasing use of mobile phones in the last decade has decreased landline telephone coverage in Spanish households. This study aimed to analyze sociodemographic characteristics and health indicators by type of telephone service (mobile phone vs. landline or landline and mobile phone). Two telephone surveys were conducted in Spanish samples (February 2010 and February 2011). Multivariate logistic regression analyses were performed to analyze differences in the main sociodemographic characteristics and health indicators according to the type of telephone service available in Spanish households. We obtained 2027 valid responses (1627 landline telephones and 400 mobile phones). Persons contacted through a mobile phone were more likely to be a foreigner, to belong to the manual social class, to have a lower educational level, and to be a smoker than those contacted through a landline telephone. The profile of the population that has only a mobile phone differs from that with a landline telephone. Therefore, telephone surveys that exclude mobile phones could show a selection bias. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Evolutionary thinking in microeconomic models: prestige bias and market bubbles.

Directory of Open Access Journals (Sweden)

Adrian Viliami Bell

Full Text Available Evolutionary models broadly support a number of social learning strategies likely important in economic behavior. Using a simple model of price dynamics, I show how prestige bias, or copying of famed (and likely successful individuals, influences price equilibria and investor disposition in a way that exacerbates or creates market bubbles. I discuss how integrating the social learning and demographic forces important in cultural evolution with economic models provides a fruitful line of inquiry into real-world behavior.

Behind the scenes: how visual memory load biases selective attention during processing of visual streams.

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Klaver, Peter; Talsma, Durk

2013-11-01

We recorded ERPs to investigate whether the visual memory load can bias visual selective attention. Participants memorized one or four letters and then responded to memory-matching letters presented in a relevant color while ignoring distractor letters or letters in an irrelevant color. Stimuli in the relevant color elicited larger frontal selection positivities (FSP) and occipital selection negativities (OSN) compared to irrelevant color stimuli. Only distractors elicited a larger FSP in the high than in the low memory load task. Memory load prolonged the OSN for all letters. Response mapping complexity was also modulated but did not affect the FSP and OSN. Together, the FSP data suggest that high memory load increased distractability. The OSN data suggest that memory load sustained attention to letters in a relevant color until working memory processing was completed, independently of whether the letters were in working memory or not. Copyright © 2013 Society for Psychophysiological Research.

The effects of self-focus on attentional biases in social anxiety:An ERP study.

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Judah, Matt R; Grant, DeMond M; Carlisle, Nancy B

2016-06-01

Cognitive theories of social anxiety disorder suggest that biased attention plays a key role in maintaining symptoms. These biases include self-focus and attention to socially threatening stimuli in the environment. The goal of this study was to utilize ERPs that are elicited by a change detection task to examine biases in selective attention (i.e., N2pc) and working memory maintenance (i.e., contralateral delay activity; CDA). Additionally, the effect of self-focus was examined using false heart rate feedback. In support of the manipulation, self-focus cues resulted in greater self-reported self-consciousness and task interference, enhanced anterior P2 amplitude and reduced SPN amplitude. Moreover, P2 amplitude for self-focus cues was correlated with reduced task performance for socially anxious subjects only. The difference in P2 amplitude between self-focus and standard cues was correlated with social anxiety independent of depression. As hypothesized, socially anxious participants (n = 20) showed early selection and maintenance of disgust faces relative to neutral faces as indicated by the N2pc and CDA components. Nonanxious controls (n = 22) did not show these biases. During self-focus cues, controls showed marginal evidence of biased selection for disgust faces, whereas socially anxious subjects showed no bias in this condition. Controls showed an ipsilateral delay activity after being cued to attend to one hemifield. Overall, this study supports early and persistent attentional bias for social threat in socially anxious individuals. Furthermore, self-focus may disrupt these biases. These findings and supplementary data are discussed in light of cognitive models of social anxiety disorder, recent empirical findings, and treatment.

CPI Bias in Korea

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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.

A Diffusion Model Analysis of Decision Biases Affecting Delayed Recognition of Emotional Stimuli

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Bowen, Holly J.; Spaniol, Julia; Patel, Ronak; Voss, Andreas

2016-01-01

Previous empirical work suggests that emotion can influence accuracy and cognitive biases underlying recognition memory, depending on the experimental conditions. The current study examines the effects of arousal and valence on delayed recognition memory using the diffusion model, which allows the separation of two decision biases thought to underlie memory: response bias and memory bias. Memory bias has not been given much attention in the literature but can provide insight into the retrieval dynamics of emotion modulated memory. Participants viewed emotional pictorial stimuli; half were given a recognition test 1-day later and the other half 7-days later. Analyses revealed that emotional valence generally evokes liberal responding, whereas high arousal evokes liberal responding only at a short retention interval. The memory bias analyses indicated that participants experienced greater familiarity with high-arousal compared to low-arousal items and this pattern became more pronounced as study-test lag increased; positive items evoke greater familiarity compared to negative and this pattern remained stable across retention interval. The findings provide insight into the separate contributions of valence and arousal to the cognitive mechanisms underlying delayed emotion modulated memory. PMID:26784108

Tie-breaker: Using language models to quantify gender bias in sports journalism

OpenAIRE

Fu, Liye; Danescu-Niculescu-Mizil, Cristian; Lee, Lillian

2016-01-01

Gender bias is an increasingly important issue in sports journalism. In this work, we propose a language-model-based approach to quantify differences in questions posed to female vs. male athletes, and apply it to tennis post-match interviews. We find that journalists ask male players questions that are generally more focused on the game when compared with the questions they ask their female counterparts. We also provide a fine-grained analysis of the extent to which the salience of this bias...

Dopamine receptor blockade attenuates the general incentive motivational effects of noncontingently delivered rewards and reward-paired cues without affecting their ability to bias action selection.

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Ostlund, Sean B; Maidment, Nigel T

2012-01-01

Environmental cues affect our behavior in a variety of ways. Despite playing an invaluable role in guiding our daily activities, such cues also appear to trigger the harmful, compulsive behaviors that characterize addiction and other disorders of behavioral control. In instrumental conditioning, rewards and reward-paired cues bias action selection and invigorate reward-seeking behaviors, and appear to do so through distinct neurobehavioral processes. Although reward-paired cues are known to invigorate performance through a dopamine-dependent incentive motivational process, it is not known if dopamine also mediates the influence of rewards and reward-paired cues over action selection. The current study contrasted the effects of systemic administration of the nonspecific dopamine receptor antagonist flupentixol on response invigoration and action bias in Pavlovian-instrumental transfer, a test of cue-elicited responding, and in instrumental reinstatement, a test of noncontingent reward-elicited responding. Hungry rats were trained on two different stimulus-outcome relationships (eg, tone-grain pellets and noise-sucrose solution) and two different action-outcome relationships (eg, left press-grain and right press-sucrose). At test, we found that flupentixol pretreatment blocked the response invigoration generated by the cues but spared their ability to bias action selection to favor the action whose outcome was signaled by the cue being presented. The response-biasing influence of noncontingent reward deliveries was also unaffected by flupentixol. Interestingly, although flupentixol had a modest effect on the immediate response invigoration produced by those rewards, it was particularly potent in countering the lingering enhancement of responding produced by multiple reward deliveries. These findings indicate that dopamine mediates the general incentive motivational effects of noncontingent rewards and reward-paired cues but does not support their ability to bias

Toward a clarification of the taxonomy of "bias" in epidemiology textbooks.

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Schwartz, Sharon; Campbell, Ulka B; Gatto, Nicolle M; Gordon, Kirsha

2015-03-01

Epidemiology textbooks typically divide biases into 3 general categories-confounding, selection bias, and information bias. Despite the ubiquity of this categorization, authors often use these terms to mean different things. This hinders communication among epidemiologists and confuses students who are just learning about the field. To understand the sources of this problem, we reviewed current general epidemiology textbooks to examine how the authors defined and categorized biases. We found that much of the confusion arises from different definitions of "validity" and from a mixing of 3 overlapping organizational features in defining and differentiating among confounding, selection bias, and information bias: consequence, the result of the problem; cause, the processes that give rise to the problem; and cure, how these biases can be addressed once they occur. By contrast, a consistent taxonomy would provide (1) a clear and consistent definition of what unites confounding, selection bias, and information bias and (2) a clear articulation and consistent application of the feature that distinguishes these categories. Based on a distillation of these textbook discussions, we provide an example of a taxonomy that we think meets these criteria.

CEO emotional bias and investment decision, Bayesian network method

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Jarboui Anis

2012-08-01

Full Text Available This research examines the determinants of firms’ investment introducing a behavioral perspective that has received little attention in corporate finance literature. The following central hypothesis emerges from a set of recently developed theories: Investment decisions are influenced not only by their fundamentals but also depend on some other factors. One factor is the biasness of any CEO to their investment, biasness depends on the cognition and emotions, because some leaders use them as heuristic for the investment decision instead of fundamentals. This paper shows how CEO emotional bias (optimism, loss aversion and overconfidence affects the investment decisions. The proposed model of this paper uses Bayesian Network Method to examine this relationship. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some 100 Tunisian executives. Our results have revealed that the behavioral analysis of investment decision implies leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its investment choices based on their ability to assess alternatives (optimism and overconfidence and risk perception (loss aversion to create of shareholder value and ensure its place at the head of the management team.

Inclusion of Body Bias Effect in SPICE Modeling of 4H-SiC Integrated Circuit Resistors

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Neudeck, Philip G.

2017-01-01

The DC electrical behavior of n-type 4H-SiC resistors used for realizing 500 degrees Celsius durable integrated circuits (ICs) is studied as a function of substrate bias and temperature. Improved fidelity electrical simulation is described using SPICE NMOS model to simulate resistor substrate body bias effect that is absent from the SPICE semiconductor resistor model.

Inclusion of Body-Bias Effect in SPICE Modeling of 4H-SiC Integrated Circuit Resistors

Science.gov (United States)

Neudeck, Philip G.

2017-01-01

The DC electrical behavior of n-type 4H-SiC resistors used for realizing 500 C durable integrated circuits (ICs) is studied as a function of substrate bias and temperature. Improved fidelity electrical simulation is described using SPICE NMOS model to simulate resistor substrate body bias effect that is absent from the SPICE semiconductor resistor model.

Inviting parents to take part in paediatric palliative care research: A mixed-methods examination of selection bias

OpenAIRE

Crocker, Joanna C; Beecham, Emma; Kelly, Paula; Dinsdale, Andrew P; Hemsley, June; Jones, Louise; Bluebond-Langner, Myra

2015-01-01

Background: Recruitment to paediatric palliative care research is challenging, with high rates of non-invitation of eligible families by clinicians. The impact on sample characteristics is unknown. Aim: To investigate, using mixed methods, non-invitation of eligible families and ensuing selection bias in an interview study about parents? experiences of advance care planning (ACP). Design: We examined differences between eligible families invited and not invited to participate by clinicians us...

Validation and selection of ODE based systems biology models: how to arrive at more reliable decisions.

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Hasdemir, Dicle; Hoefsloot, Huub C J; Smilde, Age K

2015-07-08

Most ordinary differential equation (ODE) based modeling studies in systems biology involve a hold-out validation step for model validation. In this framework a pre-determined part of the data is used as validation data and, therefore it is not used for estimating the parameters of the model. The model is assumed to be validated if the model predictions on the validation dataset show good agreement with the data. Model selection between alternative model structures can also be performed in the same setting, based on the predictive power of the model structures on the validation dataset. However, drawbacks associated with this approach are usually under-estimated. We have carried out simulations by using a recently published High Osmolarity Glycerol (HOG) pathway from S.cerevisiae to demonstrate these drawbacks. We have shown that it is very important how the data is partitioned and which part of the data is used for validation purposes. The hold-out validation strategy leads to biased conclusions, since it can lead to different validation and selection decisions when different partitioning schemes are used. Furthermore, finding sensible partitioning schemes that would lead to reliable decisions are heavily dependent on the biology and unknown model parameters which turns the problem into a paradox. This brings the need for alternative validation approaches that offer flexible partitioning of the data. For this purpose, we have introduced a stratified random cross-validation (SRCV) approach that successfully overcomes these limitations. SRCV leads to more stable decisions for both validation and selection which are not biased by underlying biological phenomena. Furthermore, it is less dependent on the specific noise realization in the data. Therefore, it proves to be a promising alternative to the standard hold-out validation strategy.

The relationship between attentional bias toward safety and driving behavior.

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Zheng, Tingting; Qu, Weina; Zhang, Kan; Ge, Yan

2016-11-01

As implicit cognitive processes garner more and more importance, studies in the fields of healthy psychology and organizational safety research have focused on attentional bias, a kind of selective allocation of attentional resources in the early stage of cognitive processing. However, few studies have explored the role of attentional bias on driving behavior. This study assessed drivers' attentional bias towards safety-related words (ABS) using the dot-probe paradigm and self-reported daily driving behaviors. The results revealed significant negative correlations between attentional bias scores and several indicators of dangerous driving. Drivers with fewer dangerous driving behaviors showed greater ABS. We also built a significant linear regression model between ABS and the total DDDI score, as well as ABS and the number of accidents. Finally, we discussed the possible mechanism underlying these associations and several limitations of our study. This study opens up a new topic for the exploration of implicit processes in driving safety research. Copyright © 2016 Elsevier Ltd. All rights reserved.

A New Navigation Satellite Clock Bias Prediction Method Based on Modified Clock-bias Quadratic Polynomial Model

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Wang, Y. P.; Lu, Z. P.; Sun, D. S.; Wang, N.

2016-01-01

In order to better express the characteristics of satellite clock bias (SCB) and improve SCB prediction precision, this paper proposed a new SCB prediction model which can take physical characteristics of space-borne atomic clock, the cyclic variation, and random part of SCB into consideration. First, the new model employs a quadratic polynomial model with periodic items to fit and extract the trend term and cyclic term of SCB; then based on the characteristics of fitting residuals, a time series ARIMA ~(Auto-Regressive Integrated Moving Average) model is used to model the residuals; eventually, the results from the two models are combined to obtain final SCB prediction values. At last, this paper uses precise SCB data from IGS (International GNSS Service) to conduct prediction tests, and the results show that the proposed model is effective and has better prediction performance compared with the quadratic polynomial model, grey model, and ARIMA model. In addition, the new method can also overcome the insufficiency of the ARIMA model in model recognition and order determination.

Measurement and control of bias in patient reported outcomes using multidimensional item response theory.

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Dowling, N Maritza; Bolt, Daniel M; Deng, Sien; Li, Chenxi

2016-05-26

Patient-reported outcome (PRO) measures play a key role in the advancement of patient-centered care research. The accuracy of inferences, relevance of predictions, and the true nature of the associations made with PRO data depend on the validity of these measures. Errors inherent to self-report measures can seriously bias the estimation of constructs assessed by the scale. A well-documented disadvantage of self-report measures is their sensitivity to response style (RS) effects such as the respondent's tendency to select the extremes of a rating scale. Although the biasing effect of extreme responding on constructs measured by self-reported tools has been widely acknowledged and studied across disciplines, little attention has been given to the development and systematic application of methodologies to assess and control for this effect in PRO measures. We review the methodological approaches that have been proposed to study extreme RS effects (ERS). We applied a multidimensional item response theory model to simultaneously estimate and correct for the impact of ERS on trait estimation in a PRO instrument. Model estimates were used to study the biasing effects of ERS on sum scores for individuals with the same amount of the targeted trait but different levels of ERS. We evaluated the effect of joint estimation of multiple scales and ERS on trait estimates and demonstrated the biasing effects of ERS on these trait estimates when used as explanatory variables. A four-dimensional model accounting for ERS bias provided a better fit to the response data. Increasing levels of ERS showed bias in total scores as a function of trait estimates. The effect of ERS was greater when the pattern of extreme responding was the same across multiple scales modeled jointly. The estimated item category intercepts provided evidence of content independent category selection. Uncorrected trait estimates used as explanatory variables in prediction models showed downward bias. A

Effects of Inventory Bias on Landslide Susceptibility Calculations

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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.

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

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Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

2013-01-01

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

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V. R. N. Pauwels

2013-09-01

Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Exchange bias of patterned systems: Model and numerical simulation

International Nuclear Information System (INIS)

Garcia, Griselda; Kiwi, Miguel; Mejia-Lopez, Jose; Ramirez, Ricardo

2010-01-01

The magnitude of the exchange bias field of patterned systems exhibits a notable increase in relation to the usual bilayer systems, where a continuous ferromagnetic film is deposited on an antiferromagnet insulator. Here we develop a model, and implement a Monte Carlo calculation, to interpret the experimental observations which is consistent with experimental results, on the basis of assuming a small fraction of spins pinned ferromagnetically in the antiferromagnetic interface layer.

Regional differences in the validity of self-reported use of health care in Belgium: selection versus reporting bias

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J. Van der Heyden

2016-08-01

Full Text Available Abstract Background The Health Care Module of the European Health Interview Survey (EHIS is aimed to obtain comparable information on the use of inpatient and ambulatory care in all EU member states. In this study we assessed the validity of self-reported information on the use of health care, collected through this instrument, in the Belgian Health Interview Survey (BHIS, and explored the impact of selection and reporting bias on the validity of regional differences in health care use observed in the BHIS. Methods To assess reporting bias, self-reported BHIS 2008 data were linked with register-based data from the Belgian compulsory health insurance (BCHI. The latter were compared with similar estimates from a random sample of the BCHI to investigate the selection bias. Outcome indicators included the prevalence of a contact with a GP, specialist, dentist and a physiotherapist, as well as inpatient and day patient hospitalisation. The validity of the estimates and the regional differences were explored through measures of agreement and logistic regression analyses. Results Validity of self-reported health care use varies by type of health service and is more affected by reporting than by selection bias. Compared to health insurance estimates, self-reported results underestimate the percentage of people with a specialist contact in the past year (50.5 % versus 65.0 % and a day patient hospitalisation (7.8 % versus 13.9 %. Inversely, survey results overestimated the percentage of people having visited a dentist in the past year: 58.3 % versus 48.6 %. The best concordance was obtained for an inpatient hospitalisation (kappa 0.75. Survey data overestimate the higher prevalence of a contact with a specialist [OR 1.51 (95 % CI 1.33–1.72 for self-report and 1.08 (95 % CI 1.05–1.15 for register] and underestimate the lower prevalence of a contact with a GP [ORs 0.59 (95 % CI 0.51–0.70 and 0.41 (95 % CI 0.39–0.42 respectively] in

Non-equilibrium scaling analysis of the Kondo model with voltage bias

International Nuclear Information System (INIS)

Fritsch, Peter; Kehrein, Stefan

2009-01-01

The quintessential description of Kondo physics in equilibrium is obtained within a scaling picture that shows the buildup of Kondo screening at low temperature. For the non-equilibrium Kondo model with a voltage bias, the key new feature are decoherence effects due to the current across the impurity. In the present paper, we show how one can develop a consistent framework for studying the non-equilibrium Kondo model within a scaling picture of infinitesimal unitary transformations (flow equations). Decoherence effects appear naturally in third order of the β-function and dominate the Hamiltonian flow for sufficiently large voltage bias. We work out the spin dynamics in non-equilibrium and compare it with finite temperature equilibrium results. In particular, we report on the behavior of the static spin susceptibility including leading logarithmic corrections and compare it with the celebrated equilibrium result as a function of temperature.

Does the Superior Colliculus Control Perceptual Sensitivity or Choice Bias during Attention? Evidence from a Multialternative Decision Framework

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Steinmetz, Nicholas A.; Moore, Tirin; Knudsen, Eric I.

2017-01-01

Distinct networks in the forebrain and the midbrain coordinate to control spatial attention. The critical involvement of the superior colliculus (SC)—the central structure in the midbrain network—in visuospatial attention has been shown by four seminal, published studies in monkeys (Macaca mulatta) performing multialternative tasks. However, due to the lack of a mechanistic framework for interpreting behavioral data in such tasks, the nature of the SC's contribution to attention remains unclear. Here we present and validate a novel decision framework for analyzing behavioral data in multialternative attention tasks. We apply this framework to re-examine the behavioral evidence from these published studies. Our model is a multidimensional extension to signal detection theory that distinguishes between two major classes of attentional mechanisms: those that alter the quality of sensory information or “sensitivity,” and those that alter the selective gating of sensory information or “choice bias.” Model-based simulations and model-based analyses of data from these published studies revealed a converging pattern of results that indicated that choice-bias changes, rather than sensitivity changes, were the primary outcome of SC manipulation. Our results suggest that the SC contributes to attentional performance predominantly by generating a spatial choice bias for stimuli at a selected location, and that this bias operates downstream of forebrain mechanisms that enhance sensitivity. The findings lead to a testable mechanistic framework of how the midbrain and forebrain networks interact to control spatial attention. SIGNIFICANCE STATEMENT Attention involves the selection of the most relevant information for differential sensory processing and decision making. While the mechanisms by which attention alters sensory encoding (sensitivity control) are well studied, the mechanisms by which attention alters decisional weighting of sensory evidence (choice-bias

Quantification of intervertebral displacement with a novel MRI-based modeling technique: Assessing measurement bias and reliability with a porcine spine model.

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Mahato, Niladri K; Montuelle, Stephane; Goubeaux, Craig; Cotton, John; Williams, Susan; Thomas, James; Clark, Brian C

2017-05-01

The purpose of this study was to develop a novel magnetic resonance imaging (MRI)-based modeling technique for measuring intervertebral displacements. Here, we present the measurement bias and reliability of the developmental work using a porcine spine model. Porcine lumbar vertebral segments were fitted in a custom-built apparatus placed within an externally calibrated imaging volume of an open-MRI scanner. The apparatus allowed movement of the vertebrae through pre-assigned magnitudes of sagittal and coronal translation and rotation. The induced displacements were imaged with static (T 1 ) and fast dynamic (2D HYCE S) pulse sequences. These images were imported into animation software, in which these images formed a background 'scene'. Three-dimensional models of vertebrae were created using static axial scans from the specimen and then transferred into the animation environment. In the animation environment, the user manually moved the models (rotoscoping) to perform model-to-'scene' matching to fit the models to their image silhouettes and assigned anatomical joint axes to the motion-segments. The animation protocol quantified the experimental translation and rotation displacements between the vertebral models. Accuracy of the technique was calculated as 'bias' using a linear mixed effects model, average percentage error and root mean square errors. Between-session reliability was examined by computing intra-class correlation coefficients (ICC) and the coefficient of variations (CV). For translation trials, a constant bias (β 0 ) of 0.35 (±0.11) mm was detected for the 2D HYCE S sequence (p=0.01). The model did not demonstrate significant additional bias with each mm increase in experimental translation (β 1 Displacement=0.01mm; p=0.69). Using the T 1 sequence for the same assessments did not significantly change the bias (p>0.05). ICC values for the T 1 and 2D HYCE S pulse sequences were 0.98 and 0.97, respectively. For rotation trials, a constant bias (�

Regression dilution bias: tools for correction methods and sample size calculation.

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Berglund, Lars

2012-08-01

Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

Bias in the Listeria monocytogenes enrichment procedure: Lineage 2 strains outcompete lineage 1 strains in University of Vermont selective enrichments

DEFF Research Database (Denmark)

Bruhn, Jesper Bartholin; Vogel, Birte Fonnesbech; Gram, Lone

2005-01-01

compounds in UVM I and II influenced this bias. The results of the present study demonstrate that the selective procedures used for isolation of L. monocytogenes may not allow a true representation of the types present in foods. Our results could have a significant impact on epidemiological studies...

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:…

PREVALENCE OF CHRONIC DISEASES IN INDIVIDUALS ASSISTED BY THE FAMILY HEALTH PROGRAM IN NITEROI, BRAZIL: EVALUATION OF SELECTION BIAS AND PROTOCOL

Directory of Open Access Journals (Sweden)

Rosa Maria Luiza G, Mesquita Evandro T, Jorge Antonio José L, Correia Dayse MS, Lugon Josemir R, Kang HC, Yokoo EM, Wahrlich V

2015-07-01

Full Text Available Background: The strategy of the Family Health Program has been used as an alternative scenario for prevalence studies. This study intended to present the protocol of the Digitalis Study (DS, prevalence study of chronic diseases, to assess sources of possible selection bias and estimate their impact on the prevalence of self-reported hypertension, diabetes, and myocardial infarction. Methods: Randomization was performed between 38 160 registered individuals with 45 to 99 years by the Family Health Program .Differences between the sources of selection bias (non-acceptance, non-attendance, substitutions were observed for gender and age. Results: Of the 1,190 residents contacted, 67.1% agreed to participate. There were 144 residents who were not randomly selected but whose participation was confirmed (substitutes. Women and individuals in the intermediate age groups and the prevalence of hypertension were higher among substitutes compared with the randomly selected individuals. Conclusion: The approach of the DS was adequate for the purposes of estimating prevalences, but there was a significant percentage of non-participation. The randomization strategy did not assume outdated records; alternative schedules for visits were not provided for; follow-up at the invitation stage was not sufficient to prevent substitutions and the inclusion of substitutes with a higher prevalence of hypertension.

Modelling of radial electric fields and currents during divertor plate biasing on TdeV

International Nuclear Information System (INIS)

Lachambre, J.L.; Quirion, B.; Boucher, C.

1994-01-01

A simple model based on non-ambipolar radial transport and planar sheath physics is used to describe the generation of radial electric fields and currents in the scrape-off layer of the Tokamak de Varennes (TdeV) during divertor plate biasing. In general, the calculated predictions compare favourably with TdeV results over a variety of plasma conditions and divertor magnetic configurations. Validated by the experiment, the model is used to study the scaling laws of perpendicular ion mobility and to test existing related theories. Finally, the model is proposed as a useful tool for the design and upgrade of biased divertors through optimization of the plate and throat geometry. (author). 35 refs, 16 figs, 1 tab

Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection

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Brunetti, Carlotta; Linde, Niklas

2018-01-01

Quantitative hydrogeophysical studies rely heavily on petrophysical relationships that link geophysical properties to hydrogeological properties and state variables. Coupled inversion studies are frequently based on the questionable assumption that these relationships are perfect (i.e., no scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical uncertainty on inferred posterior porosity and hydraulic conductivity distributions and on Bayes factors used in Bayesian model selection. Our study shows that accounting for petrophysical uncertainty in the inversion (I) decreases bias of the inferred variance of hydrogeological subsurface properties, (II) provides more realistic uncertainty assessment and (III) reduces the overconfidence in the ability of geophysical data to falsify conceptual hydrogeological models.

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

Science.gov (United States)

Zhang, Chengzhu; Xie, Shaocheng; Klein, Stephen A.; Ma, Hsi-yen; Tang, Shuaiqi; Van Weverberg, Kwinten; Morcrette, Cyril J.; Petch, Jon

2018-03-01

All the weather and climate models participating in the Clouds Above the United States and Errors at the Surface project show a summertime surface air temperature (T2 m) warm bias in the region of the central United States. To understand the warm bias in long-term climate simulations, we assess the Atmospheric Model Intercomparison Project simulations from the Coupled Model Intercomparison Project Phase 5, with long-term observations mainly from the Atmospheric Radiation Measurement program Southern Great Plains site. Quantities related to the surface energy and water budget, and large-scale circulation are analyzed to identify possible factors and plausible links involved in the warm bias. The systematic warm season bias is characterized by an overestimation of T2 m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear-sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2 m through its control on evaporative fraction. The error in evaporative fraction is another important contributor to T2 m. Similar sources of error are found in hindcast from other Clouds Above the United States and Errors at the Surface studies. In Atmospheric Model Intercomparison Project simulations, biases in meridional wind velocity associated with the low-level jet and the 500 hPa vertical velocity may also relate to T2 m bias through their control on the surface energy and water budget.

Bias due to sample selection in propensity score matching for a supportive housing program evaluation in New York City.

Directory of Open Access Journals (Sweden)

Sungwoo Lim

Full Text Available OBJECTIVES: Little is known about influences of sample selection on estimation in propensity score matching. The purpose of the study was to assess potential selection bias using one-to-one greedy matching versus optimal full matching as part of an evaluation of supportive housing in New York City (NYC. STUDY DESIGN AND SETTINGS: Data came from administrative data for 2 groups of applicants who were eligible for an NYC supportive housing program in 2007-09, including chronically homeless adults with a substance use disorder and young adults aging out of foster care. We evaluated the 2 matching methods in their ability to balance covariates and represent the original population, and in how those methods affected outcomes related to Medicaid expenditures. RESULTS: In the population with a substance use disorder, only optimal full matching performed well in balancing covariates, whereas both methods created representative populations. In the young adult population, both methods balanced covariates effectively, but only optimal full matching created representative populations. In the young adult population, the impact of the program on Medicaid expenditures was attenuated when one-to-one greedy matching was used, compared with optimal full matching. CONCLUSION: Given covariate balancing with both methods, attenuated program impacts in the young adult population indicated that one-to-one greedy matching introduced selection bias.

Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling.

Science.gov (United States)

El-Gabbas, Ahmed; Dormann, Carsten F

2018-02-01

Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence-only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species-specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point-process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor ("prior") to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias-free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data

Bias-dependent model of the electrical impedance of ionic polymer-metal composites.

Science.gov (United States)

Cha, Youngsu; Porfiri, Maurizio

2013-02-01

In this paper, we analyze the charge dynamics of ionic polymer-metal composites (IPMCs) in response to voltage inputs composed of a large dc bias and a small superimposed time-varying voltage. The IPMC chemoelectrical behavior is described through the modified Poisson-Nernst-Planck framework, in which steric effects are taken into consideration. The physics of charge build-up and mass transfer in the proximity of the high surface electrodes is modeled by schematizing the IPMC as the stacked sequence of five layers, in which the ionomeric membrane is separated from the metal electrodes by two composite layers. The method of matched asymptotic expansions is used to derive a semianalytical solution for the concentration of mobile counterions and the electric potential in the IPMC, which is, in turn, used to establish an equivalent circuit model for the IPMC electrical response. The circuit model consists of the series connection of a resistor and two complex elements, each constituted by the parallel connection of a capacitor and a Warburg impedance. The resistor is associated with ion transport in the ionomeric membrane and is independent of the dc bias. The capacitors and the Warburg impedance idealize charge build-up and mass transfer in the vicinity of the electrodes and their value is controlled by the dc bias. The proposed approach is validated against experimental results on in-house fabricated IPMCs and the accuracy of the equivalent circuit is assessed through comparison with finite element results.

Application of Probabilistic Multiple-Bias Analyses to a Cohort- and a Case-Control Study on the Association between Pandemrix™ and Narcolepsy.

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Kaatje Bollaerts

Full Text Available An increase in narcolepsy cases was observed in Finland and Sweden towards the end of the 2009 H1N1 influenza pandemic. Preliminary observational studies suggested a temporal link with the pandemic influenza vaccine Pandemrix™, leading to a number of additional studies across Europe. Given the public health urgency, these studies used readily available retrospective data from various sources. The potential for bias in such settings was generally acknowledged. Although generally advocated by key opinion leaders and international health authorities, no systematic quantitative assessment of the potential joint impact of biases was undertaken in any of these studies.We applied bias-level multiple-bias analyses to two of the published narcolepsy studies: a pediatric cohort study from Finland and a case-control study from France. In particular, we developed Monte Carlo simulation models to evaluate a potential cascade of biases, including confounding by age, by indication and by natural H1N1 infection, selection bias, disease- and exposure misclassification. All bias parameters were evidence-based to the extent possible.Given the assumptions used for confounding, selection bias and misclassification, the Finnish rate ratio of 13.78 (95% CI: 5.72-28.11 reduced to a median value of 6.06 (2.5th- 97.5th percentile: 2.49-15.1 and the French odds ratio of 5.43 (95% CI: 2.6-10.08 to 1.85 (2.5th-97.5th percentile: 0.85-4.08.We illustrate multiple-bias analyses using two studies on the Pandemrix™-narcolepsy association and advocate their use to better understand the robustness of study findings. Based on our multiple-bias models, the observed Pandemrix™-narcolepsy association consistently persists in the Finnish study. For the French study, the results of our multiple-bias models were inconclusive.

Invited Commentary: Agent-Based Models-Bias in the Face of Discovery.

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Keyes, Katherine M; Tracy, Melissa; Mooney, Stephen J; Shev, Aaron; Cerdá, Magdalena

2017-07-15

Agent-based models (ABMs) have grown in popularity in epidemiologic applications, but the assumptions necessary for valid inference have only partially been articulated. In this issue, Murray et al. (Am J Epidemiol. 2017;186(2):131-142) provided a much-needed analysis of the consequence of some of these assumptions, comparing analysis using an ABM to a similar analysis using the parametric g-formula. In particular, their work focused on the biases that can arise in ABMs that use parameters drawn from distinct populations whose causal structures and baseline outcome risks differ. This demonstration of the quantitative issues that arise in transporting effects between populations has implications not only for ABMs but for all epidemiologic applications, because making use of epidemiologic results requires application beyond a study sample. Broadly, because health arises within complex, dynamic, and hierarchical systems, many research questions cannot be answered statistically without strong assumptions. It will require every tool in our store of methods to properly understand population dynamics if we wish to build an evidence base that is adequate for action. Murray et al.'s results provide insight into these assumptions that epidemiologists can use when selecting a modeling approach. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Bias against research on gender bias.

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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.

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.

Analysis of the synonymous codon usage bias in recently emerged enterovirus D68 strains.

Science.gov (United States)

Karniychuk, Uladzimir U

2016-09-02

Understanding the codon usage pattern of a pathogen and relationship between pathogen and host's codon usage patterns has fundamental and applied interests. Enterovirus D68 (EV-D68) is an emerging pathogen with a potentially high public health significance. In the present study, the synonymous codon usage bias of 27 recently emerged, and historical EV-D68 strains was analyzed. In contrast to previously studied enteroviruses (enterovirus 71 and poliovirus), EV-D68 and human host have a high discrepancy between favored codons. Analysis of viral synonymous codon usage bias metrics, viral nucleotide/dinucleotide compositional parameters, and viral protein properties showed that mutational pressure is more involved in shaping the synonymous codon usage bias of EV-D68 than translation selection. Computation of codon adaptation indices allowed to estimate expression potential of the EV-D68 genome in several commonly used laboratory animals. This approach requires experimental validation and may provide an auxiliary tool for the rational selection of laboratory animals to model emerging viral diseases. Enterovirus D68 genome compositional and codon usage data can be useful for further pathogenesis, animal model, and vaccine design studies. Copyright © 2016 Elsevier B.V. All rights reserved.

Addressing catch mechanisms in gillnets improves modeling of selectivity and estimates of mortality rates: a case study using survey data on an endangered stock of Arctic char

DEFF Research Database (Denmark)

Jonsson, T.; Setzer, M.; Pope, John George

2013-01-01

Estimation of fish stock size distributions from survey data requires knowledge about gear selectivity. However, selectivity models rest on assumptions that seldom are analyzed. Departures from these can lead to misinterpretations and biased management recommendations. Here, we use survey data...... and asymmetric, with poor model fits. Removing potentially nonmeshed fish had the greatest positive effect on model fit, resulting in much narrower and less asymmetric selection curves, while attempting to take nonisometric growth into account, by using girth rather than length, improved model fit...

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

Science.gov (United States)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

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.

Quasi-experimental study designs series-paper 6: risk of bias assessment.

Science.gov (United States)

Waddington, Hugh; Aloe, Ariel M; Becker, Betsy Jane; Djimeu, Eric W; Hombrados, Jorge Garcia; Tugwell, Peter; Wells, George; Reeves, Barney

2017-09-01

Rigorous and transparent bias assessment is a core component of high-quality systematic reviews. We assess modifications to existing risk of bias approaches to incorporate rigorous quasi-experimental approaches with selection on unobservables. These are nonrandomized studies using design-based approaches to control for unobservable sources of confounding such as difference studies, instrumental variables, interrupted time series, natural experiments, and regression-discontinuity designs. We review existing risk of bias tools. Drawing on these tools, we present domains of bias and suggest directions for evaluation questions. The review suggests that existing risk of bias tools provide, to different degrees, incomplete transparent criteria to assess the validity of these designs. The paper then presents an approach to evaluating the internal validity of quasi-experiments with selection on unobservables. We conclude that tools for nonrandomized studies of interventions need to be further developed to incorporate evaluation questions for quasi-experiments with selection on unobservables. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

An analytical nonlinear magnetoelectric coupling model of laminated composites under combined pre-stress and magnetic bias loadings

International Nuclear Information System (INIS)

Zhou, Hao-Miao; Qu, Shao-Xing; Ou, Xiao-Wei; Xiao, Ying; Wu, Hua-Ping

2013-01-01

Based on the equivalent circuit method, this paper adopts the nonlinear magnetostrictive constitutive relations to establish an analytical nonlinear magnetoelectric coefficient model for magnetostrictive/piezoelectric/magnetostrictive laminated magnetoelectric composites. When the pre-stress is set to zero in the model, the predicted results of the magnetoelectric coefficient coincide well with the available experimental results both qualitatively and quantitatively. Using the model, we can qualitatively predict the influence of the pre-stress, magnetic bias fields and the volume fraction of the magnetostrictive material on the magnetoelectric coefficient. The predicted results show that the influences of the pre-stress on the magnetoelectric coefficient, which varies with the magnetic bias field, before and after reaching the magnetoelectric coefficient maximum, are opposite. That is, the influence of the pre-stress on curves of the magnetoelectric coefficient reverses when the magnetoelectric coefficient reaches its maximum. Therefore, the correct setting of the pre-stress can lower the applied magnetic bias field and improve the magnetoelectric coefficient. The established nonlinear magnetoelectric effect model can provide a theoretical basis for regulating the magnetoelectric coefficient by the pre-stress and magnetic bias field and make it possible to design high-precision miniature magnetoelectric devices. (paper)

Modeling the influence of investigator bias on the elicitation of true and false confessions.

Science.gov (United States)

Narchet, Fadia M; Meissner, Christian A; Russano, Melissa B

2011-12-01

The aim of this study was to model various social and cognitive processes believed to be associated with true and false confessions by exploring the link between investigative biases and what occurs in the interrogation room. Using the Russano et al. (Psychol Sci 16:481-486, 2005) paradigm, this study explored how perceptions of guilt influenced the frequency and type of interrogation tactics used, suspect's perceptions of the interrogation process, the likelihood of confession, and investigator's resulting perceptions of culpability. Results suggested that investigator bias led to the increased use of minimization tactics and thereby increased the likelihood of false confessions by innocent participants. In contrast, the manipulation of investigator bias had no direct or indirect influence on guilty participants. These findings confirm the important role of investigator bias and improve our understanding of the decision-making process associated with true and false confessions.

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

Directory of Open Access Journals (Sweden)

M. E. Gorbunov

2018-01-01

Full Text Available A new reference occultation processing system (rOPS will include a Global Navigation Satellite System (GNSS radio occultation (RO retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA retrieval in the lower troposphere and introduce (1 an empirically estimated boundary layer bias (BLB model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2 the estimation of (residual systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors, where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

Science.gov (United States)

Gorbunov, Michael E.; Kirchengast, Gottfried

2018-01-01

A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

Science.gov (United States)

Vansteelandt, Stijn; Walter, Stefan; Tchetgen Tchetgen, Eric

2018-07-01

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models.

Science.gov (United States)

Borup, Morten; Grum, Morten; Mikkelsen, Peter Steen

2013-01-01

When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

Selective forces and mutational biases drive stop codon usage in the human genome: a comparison with sense codon usage.

Science.gov (United States)

Trotta, Edoardo

2016-05-17

The three stop codons UAA, UAG, and UGA signal the termination of mRNA translation. As a result of a mechanism that is not adequately understood, they are normally used with unequal frequencies. In this work, we showed that selective forces and mutational biases drive stop codon usage in the human genome. We found that, in respect to sense codons, stop codon usage was affected by stronger selective forces but was less influenced by neutral mutational biases. UGA is the most frequent termination codon in human genome. However, UAA was the preferred stop codon in genes with high breadth of expression, high level of expression, AT-rich coding sequences, housekeeping functions, and in gene ontology categories with the largest deviation from expected stop codon usage. Selective forces associated with the breadth and the level of expression favoured AT-rich sequences in the mRNA region including the stop site and its proximal 3'-UTR, but acted with scarce effects on sense codons, generating two regions, upstream and downstream of the stop codon, with strongly different base composition. By favouring low levels of GC-content, selection promoted labile local secondary structures at the stop site and its proximal 3'-UTR. The compositional and structural context favoured by selection was surprisingly emphasized in the class of ribosomal proteins and was consistent with sequence elements that increase the efficiency of translational termination. Stop codons were also heterogeneously distributed among chromosomes by a mechanism that was strongly correlated with the GC-content of coding sequences. In human genome, the nucleotide composition and the thermodynamic stability of stop codon site and its proximal 3'-UTR are correlated with the GC-content of coding sequences and with the breadth and the level of gene expression. In highly expressed genes stop codon usage is compositionally and structurally consistent with highly efficient translation termination signals.

Bias corrrection in the dynamic panel data model with a nonscalar disturbance covariance matrix

NARCIS (Netherlands)

Bun, M.J.G.

2001-01-01

Approximation formulae are developed for the bias of ordinary andgeneralized Least Squares Dummy Variable (LSDV) estimators in dynamicpanel data models. Results from Kiviet (1995, 1999) are extended tohigher-order dynamic panel data models with general covariancestructure. The focus is on estimation

Selected properties of GPS and Galileo-IOV receiver intersystem biases in multi-GNSS data processing

International Nuclear Information System (INIS)

Paziewski, Jacek; Sieradzki, Rafał; Wielgosz, Paweł

2015-01-01

Two overlapping frequencies—L1/E1 and L5/E5a—in GPS and Galileo systems support the creation of mixed double-differences in a tightly combined relative positioning model. On the other hand, a tightly combined model makes it necessary to take into account receiver intersystem bias, which is the difference in receiver hardware delays. This bias is present in both carrier-phase and pseudorange observations. Earlier research showed that using a priori knowledge of earlier-calibrated ISB to correct GNSS observations has significant impact on ambiguity resolution and, therefore, precise positioning results. In previous research concerning ISB estimation conducted by the authors, small oscillations in phase ISB time series were detected. This paper investigates this effect present in the GPS–Galileo-IOV ISB time series. In particular, ISB short-term temporal stability and its dependence on the number of Galileo satellites used in the ISB estimation was examined. In this contribution we investigate the amplitude and frequency of the detected ISB time series oscillations as well as their potential source. The presented results are based on real observational data collected on a zero baseline with the use of different sets of GNSS receivers. (paper)

Selected properties of GPS and Galileo-IOV receiver intersystem biases in multi-GNSS data processing

Science.gov (United States)

Paziewski, Jacek; Sieradzki, Rafał; Wielgosz, Paweł

2015-09-01

Two overlapping frequencies—L1/E1 and L5/E5a—in GPS and Galileo systems support the creation of mixed double-differences in a tightly combined relative positioning model. On the other hand, a tightly combined model makes it necessary to take into account receiver intersystem bias, which is the difference in receiver hardware delays. This bias is present in both carrier-phase and pseudorange observations. Earlier research showed that using a priori knowledge of earlier-calibrated ISB to correct GNSS observations has significant impact on ambiguity resolution and, therefore, precise positioning results. In previous research concerning ISB estimation conducted by the authors, small oscillations in phase ISB time series were detected. This paper investigates this effect present in the GPS-Galileo-IOV ISB time series. In particular, ISB short-term temporal stability and its dependence on the number of Galileo satellites used in the ISB estimation was examined. In this contribution we investigate the amplitude and frequency of the detected ISB time series oscillations as well as their potential source. The presented results are based on real observational data collected on a zero baseline with the use of different sets of GNSS receivers.

Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model

Science.gov (United States)

Meng, X.; Lyu, S.; Zhang, T.; Zhao, L.; Li, Z.; Han, B.; Li, S.; Ma, D.; Chen, H.; Ao, Y.; Luo, S.; Shen, Y.; Guo, J.; Wen, L.

2018-04-01

Systematic cold biases exist in the simulation for 2 m air temperature in the Tibetan Plateau (TP) when using regional climate models and global atmospheric general circulation models. We updated the albedo in the Weather Research and Forecasting (WRF) Model lower boundary condition using the Global LAnd Surface Satellite Moderate-Resolution Imaging Spectroradiometer albedo products and demonstrated evident improvement for cold temperature biases in the TP. It is the large overestimation of albedo in winter and spring in the WRF model that resulted in the large cold temperature biases. The overestimated albedo was caused by the simulated precipitation biases and over-parameterization of snow albedo. Furthermore, light-absorbing aerosols can result in a large reduction of albedo in snow and ice cover. The results suggest the necessity of developing snow albedo parameterization using observations in the TP, where snow cover and melting are very different from other low-elevation regions, and the influence of aerosols should be considered as well. In addition to defining snow albedo, our results show an urgent call for improving precipitation simulation in the TP.

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.

Cognitive bias in back pain patients attending osteopathy: testing the enmeshment model in reference to future thinking.

Science.gov (United States)

Read, Jessica; Pincus, Tamar

2004-12-01

Depressive symptoms are common in chronic pain. Previous research has found differences in information-processing biases in depressed pain patients and depressed people without pain. The schema enmeshment model of pain (SEMP) has been proposed to explain chronic pain patients' information-processing biases. Negative future thinking is common in depression but has not been explored in relation to chronic pain and information-processing models. The study aimed to test the SEMP with reference to future thinking. An information-processing paradigm compared endorsement and recall bias between depressed and non-depressed chronic low back pain patients and control participants. Twenty-five depressed and 35 non-depressed chronic low back pain patients and 25 control participants (student osteopaths) were recruited from an osteopathy practice. Participants were asked to endorse positive and negative ill-health, depression-related, and neutral (control) adjectives, encoded in reference to either current or future time-frame. Incidental recall of the adjectives was then tested. While the expected hypothesis of a recall bias by depressed pain patients towards ill-health stimuli in the current condition was confirmed, the recall bias was not present in the future condition. Additionally, patterns of endorsement and recall bias differed. Results extend understanding of future thinking in chronic pain within the context of the SEMP.

Multisite bias correction of precipitation data from regional climate models

Czech Academy of Sciences Publication Activity Database

Hnilica, Jan; Hanel, M.; Puš, V.

2017-01-01

Roč. 37, č. 6 (2017), s. 2934-2946 ISSN 0899-8418 R&D Projects: GA ČR GA16-05665S Grant - others:Grantová agentura ČR - GA ČR(CZ) 16-16549S Institutional support: RVO:67985874 Keywords : bias correction * regional climate model * correlation * covariance * multivariate data * multisite correction * principal components * precipitation Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Climatic research Impact factor: 3.760, year: 2016

Looking on the bright side: biased attention and the human serotonin transporter gene.

Science.gov (United States)

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-05-22

Humans differ in terms of biased attention for emotional stimuli and these biases can confer differential resilience and vulnerability to emotional disorders. Selective processing of positive emotional information, for example, is associated with enhanced sociability and well-being while a bias for negative material is associated with neuroticism and anxiety. A tendency to selectively avoid negative material might also be associated with mental health and well-being. The neurobiological mechanisms underlying these cognitive phenotypes are currently unknown. Here we show for the first time that allelic variation in the promotor region of the serotonin transporter gene (5-HTTLPR) is associated with differential biases for positive and negative affective pictures. Individuals homozygous for the long allele (LL) showed a marked bias to selectively process positive affective material alongside selective avoidance of negative affective material. This potentially protective pattern was absent among individuals carrying the short allele (S or SL). Thus, allelic variation on a common genetic polymorphism was associated with the tendency to selectively process positive or negative information. The current study is important in demonstrating a genotype-related alteration in a well-established processing bias, which is a known risk factor in determining both resilience and vulnerability to emotional disorders.

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.

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).

Application of Photocurrent Model on Polymer Solar Cells Under Forward Bias Stress

DEFF Research Database (Denmark)

Rizzo, Antonio; Torto, Lorenzo; Wrachien, Nicola

2017-01-01

We performed a constant current stress at forward bias on organic heterojunction solar cells. We measured current voltage curves in both dark and light at each stress step to calculate the photocurrent. An existing model applied to photocurrent experimental data allows the estimation of several...

Automation of Endmember Pixel Selection in SEBAL/METRIC Model

Science.gov (United States)

Bhattarai, N.; Quackenbush, L. J.; Im, J.; Shaw, S. B.

2015-12-01

The commonly applied surface energy balance for land (SEBAL) and its variant, mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC) models require manual selection of endmember (i.e. hot and cold) pixels to calibrate sensible heat flux. Current approaches for automating this process are based on statistical methods and do not appear to be robust under varying climate conditions and seasons. In this paper, we introduce a new approach based on simple machine learning tools and search algorithms that provides an automatic and time efficient way of identifying endmember pixels for use in these models. The fully automated models were applied on over 100 cloud-free Landsat images with each image covering several eddy covariance flux sites in Florida and Oklahoma. Observed land surface temperatures at automatically identified hot and cold pixels were within 0.5% of those from pixels manually identified by an experienced operator (coefficient of determination, R2, ≥ 0.92, Nash-Sutcliffe efficiency, NSE, ≥ 0.92, and root mean squared error, RMSE, ≤ 1.67 K). Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.91 and RMSE ≤ 0.35 mm day-1). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed approach should reduce time demands for applying SEBAL/METRIC models and allow for their more widespread and frequent use. This automation can also reduce potential bias that could be introduced by an inexperienced operator and extend the domain of the models to new users.

The effects of juvenile stress on anxiety, cognitive bias and decision making in adulthood: a rat model.

Directory of Open Access Journals (Sweden)

Nichola M Brydges

Full Text Available Stress experienced in childhood is associated with an increased risk of developing psychiatric disorders in adulthood. These disorders are particularly characterized by disturbances to emotional and cognitive processes, which are not currently fully modeled in animals. Assays of cognitive bias have recently been used with animals to give an indication of their emotional/cognitive state. We used a cognitive bias test, alongside a traditional measure of anxiety (elevated plus maze, to investigate the effects of juvenile stress (JS on adulthood behaviour using a rodent model. During the cognitive bias test, animals were trained to discriminate between two reward bowls based on a stimulus (rough/smooth sandpaper encountered before they reached the bowls. One stimulus (e.g. rough was associated with a lower value reward than the other (e.g. smooth. Once rats were trained, their cognitive bias was explored through the presentation of an ambiguous stimulus (intermediate grade sandpaper: a rat was classed as optimistic if it chose the bowl ordinarily associated with the high value reward. JS animals were lighter than controls, exhibited increased anxiety-like behaviour in the elevated plus maze and were more optimistic in the cognitive bias test. This increased optimism may represent an optimal foraging strategy for these underweight animals. JS animals were also faster than controls to make a decision when presented with an ambiguous stimulus, suggesting altered decision making. These results demonstrate that stress in the juvenile phase can increase anxiety-like behaviour and alter cognitive bias and decision making in adulthood in a rat model.

Cultural selection drives the evolution of human communication systems.

Science.gov (United States)

Tamariz, Monica; Ellison, T Mark; Barr, Dale J; Fay, Nicolas

2014-08-07

Human communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that communication variants spread in a population following neutral evolutionary dynamics (also known as drift models), we tested the role of two cultural selection models: coordination- and content-biased. We constructed a parametrized mixed probabilistic model of the spread of communicative variants in four 8-person laboratory micro-societies engaged in a simple communication game. We found that selectionist models, working in combination, explain the majority of the empirical data. The best-fitting parameter setting includes an egocentric bias and a content bias, suggesting that participants retained their own previously used communicative variants unless they encountered a superior (content-biased) variant, in which case it was adopted. This novel pattern of results suggests that (i) a theory of the cultural evolution of human communication systems must integrate selectionist models and (ii) human communication systems are functionally adaptive complex systems.

Using hidden Markov models to deal with availability bias on line transect surveys.

Science.gov (United States)

Borchers, D L; Zucchini, W; Heide-Jørgensen, M P; Cañadas, A; Langrock, R

2013-09-01

We develop estimators for line transect surveys of animals that are stochastically unavailable for detection while within detection range. The detection process is formulated as a hidden Markov model with a binary state-dependent observation model that depends on both perpendicular and forward distances. This provides a parametric method of dealing with availability bias when estimates of availability process parameters are available even if series of availability events themselves are not. We apply the estimators to an aerial and a shipboard survey of whales, and investigate their properties by simulation. They are shown to be more general and more flexible than existing estimators based on parametric models of the availability process. We also find that methods using availability correction factors can be very biased when surveys are not close to being instantaneous, as can estimators that assume temporal independence in availability when there is temporal dependence. © 2013, The International Biometric Society.

Worry or craving? A selective review of evidence for food-related attention biases in obese individuals, eating-disorder patients, restrained eaters and healthy samples.

Science.gov (United States)

Werthmann, Jessica; Jansen, Anita; Roefs, Anne

2015-05-01

Living in an 'obesogenic' environment poses a serious challenge for weight maintenance. However, many people are able to maintain a healthy weight indicating that not everybody is equally susceptible to the temptations of this food environment. The way in which someone perceives and reacts to food cues, that is, cognitive processes, could underlie differences in susceptibility. An attention bias for food could be such a cognitive factor that contributes to overeating. However, an attention bias for food has also been implicated with restrained eating and eating-disorder symptomatology. The primary aim of the present review was to determine whether an attention bias for food is specifically related to obesity while also reviewing evidence for attention biases in eating-disorder patients, restrained eaters and healthy-weight individuals. Another aim was to systematically examine how selective attention for food relates (causally) to eating behaviour. Current empirical evidence on attention bias for food within obese samples, eating-disorder patients, and, even though to a lesser extent, in restrained eaters is contradictory. However, present experimental studies provide relatively consistent evidence that an attention bias for food contributes to subsequent food intake. This review highlights the need to distinguish not only between different (temporal) attention bias components, but also to take different motivations (craving v. worry) and their impact on attentional processing into account. Overall, the current state of research suggests that biased attention could be one important cognitive mechanism by which the food environment tempts us into overeating.

Seasonal and circadian biases in bird tracking with solar GPS-tags.

Directory of Open Access Journals (Sweden)

Rafa Silva

Full Text Available Global Positioning System (GPS tags are nowadays widely used in wildlife tracking. This geolocation technique can suffer from fix loss biases due to poor satellite GPS geometry, that result in tracking data gaps leading to wrong research conclusions. In addition, new solar-powered GPS tags deployed on birds can suffer from a new "battery drain bias" currently ignored in movement ecology analyses. We use a GPS tracking dataset of bearded vultures (Gypaetus barbatus, tracked for several years with solar GPS tags, to evaluate the causes and triggers of fix and data retrieval loss biases. We compare two models of solar GPS tags using different data retrieval systems (Argos vs GSM-GPRS, and programmed with different duty cycles. Neither of the models was able to accomplish the duty cycle programed initially. Fix and data retrieval loss rates were always greater than expected, and showed non-random gaps in GPS locations. Number of fixes per month of tracking was a bad criterion to identify tags with smaller biases. Fix-loss rates were four times higher due to battery drain than due to poor GPS satellite geometry. Both tag models were biased due to the uneven solar energy available for the recharge of the tag throughout the annual cycle, resulting in greater fix-loss rates in winter compared to summer. In addition, we suggest that the bias found along the diurnal cycle is linked to a complex three-factor interaction of bird flight behavior, topography and fix interval. More fixes were lost when vultures were perching compared to flying, in rugged versus flat topography. But long fix-intervals caused greater loss of fixes in dynamic (flying versus static situations (perching. To conclude, we emphasize the importance of evaluating fix-loss bias in current tracking projects, and deploying GPS tags that allow remote duty cycle updates so that the most appropriate fix and data retrieval intervals can be selected.

Selective reporting bias of harm outcomes within studies: findings from a cohort of systematic reviews.

Science.gov (United States)

Saini, Pooja; Loke, Yoon K; Gamble, Carrol; Altman, Douglas G; Williamson, Paula R; Kirkham, Jamie J

2014-11-21

To determine the extent and nature of selective non-reporting of harm outcomes in clinical studies that were eligible for inclusion in a cohort of systematic reviews. Cohort study of systematic reviews from two databases. Outcome reporting bias in trials for harm outcomes (ORBIT II) in systematic reviews from the Cochrane Library and a separate cohort of systematic reviews of adverse events. 92 systematic reviews of randomised controlled trials and non-randomised studies published in the Cochrane Library between issue 9, 2012 and issue 2, 2013 (Cochrane cohort) and 230 systematic reviews published between 1 January 2007 and 31 December 2011 in other publications, synthesising data on harm outcomes (adverse event cohort). A 13 point classification system for missing outcome data on harm was developed and applied to the studies. 86% (79/92) of reviews in the Cochrane cohort did not include full data from the main harm outcome of interest of each review for all of the eligible studies included within that review; 76% (173/230) for the adverse event cohort. Overall, the single primary harm outcome was inadequately reported in 76% (705/931) of the studies included in the 92 reviews from the Cochrane cohort and not reported in 47% (4159/8837) of the 230 reviews in the adverse event cohort. In a sample of primary studies not reporting on the single primary harm outcome in the review, scrutiny of the study publication revealed that outcome reporting bias was suspected in nearly two thirds (63%, 248/393). The number of reviews suspected of outcome reporting bias as a result of missing or partially reported harm related outcomes from at least one eligible study is high. The declaration of important harms and the quality of the reporting of harm outcomes must be improved in both primary studies and systematic reviews. © Saini et al 2014.

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.

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

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.

Procedures for Dealing with Optimism Bias in Transport Planning

DEFF Research Database (Denmark)

Flyvbjerg, Bent; Glenting, Carsten; Rønnest, Arne Kvist

of the document are to provide empirically based optimism bias up-lifts for selected reference classes of transport infrastructure projects and provide guidance on using the established uplifts to produce more realistic forecasts for the individual project's capital expenditures. Furthermore, the underlying...... causes and institutional context for optimism bias in British transport projects are discussed and some possibilities for reducing optimism bias in project preparation and decision-making are identified....

Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media

DEFF Research Database (Denmark)

Cooley, R.L.; Christensen, Steen

2006-01-01

small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear...... are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis....

Post-model selection inference and model averaging

Directory of Open Access Journals (Sweden)

Georges Nguefack-Tsague

2011-07-01

Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.

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

Nonresponse Bias and Superpopulation Models in Electoral Polls

Directory of Open Access Journals (Sweden)

Jose M. Pavía

2012-01-01

Full Text Available Nonresponse bias (and, to a lesser extent, measurement error has become the main source of error for electoral forecasts in Spain. Although the post-stratifi cation techniques and ratio estimators currently used in the polling industry reduce deviations, they do not show enough capacity to mend the biases introduced when collecting data. This research reveals how a more effi cient use of the electoral information available outside the sample could help to signifi cantly improve the accuracy of predictions, and uses simulation techniques to show that this may be accompanied by less expensive sampling designs. The analysis, nevertheless, also concludes that the proposed specifi cation is not a panacea and affi rms that there is still scope for reducing nonresponse bias, pointing to several issues for future research.

A kinetic model of trp-cage folding from multiple biased molecular dynamics simulations.

Directory of Open Access Journals (Sweden)

Fabrizio Marinelli

2009-08-01

Full Text Available Trp-cage is a designed 20-residue polypeptide that, in spite of its size, shares several features with larger globular proteins.Although the system has been intensively investigated experimentally and theoretically, its folding mechanism is not yet fully understood. Indeed, some experiments suggest a two-state behavior, while others point to the presence of intermediates. In this work we show that the results of a bias-exchange metadynamics simulation can be used for constructing a detailed thermodynamic and kinetic model of the system. The model, although constructed from a biased simulation, has a quality similar to those extracted from the analysis of long unbiased molecular dynamics trajectories. This is demonstrated by a careful benchmark of the approach on a smaller system, the solvated Ace-Ala3-Nme peptide. For theTrp-cage folding, the model predicts that the relaxation time of 3100 ns observed experimentally is due to the presence of a compact molten globule-like conformation. This state has an occupancy of only 3% at 300 K, but acts as a kinetic trap.Instead, non-compact structures relax to the folded state on the sub-microsecond timescale. The model also predicts the presence of a state at Calpha-RMSD of 4.4 A from the NMR structure in which the Trp strongly interacts with Pro12. This state can explain the abnormal temperature dependence of the Pro12-delta3 and Gly11-alpha3 chemical shifts. The structures of the two most stable misfolded intermediates are in agreement with NMR experiments on the unfolded protein. Our work shows that, using biased molecular dynamics trajectories, it is possible to construct a model describing in detail the Trp-cage folding kinetics and thermodynamics in agreement with experimental data.

Assessing the Regional/Diurnal Bias between Satellite Retrievals and GEOS-5/MERRA Model Estimates of Land Surface Temperature

Science.gov (United States)

Scarino, B. R.; Smith, W. L., Jr.; Minnis, P.; Bedka, K. M.

2017-12-01

Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Continuous remote sensing of the Earth's energy budget, as conducted by the Clouds and Earth's Radiant Energy System (CERES) project, allows for near-realtime evaluation of cloud and surface radiation properties. It is unfortunately common for there to be bias between atmospheric/surface radiation models and Earth-observations. For example, satellite-observed surface skin temperature (Ts), an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface, can be biased due to atmospheric adjustment assumptions and anisotropy effects. Similarly, models are potentially biased by errors in initial conditions and regional forcing assumptions, which can be mitigated through assimilation with true measurements. As such, when frequent, broad-coverage, and accurate retrievals of satellite Ts are available, important insights into model estimates of Ts can be gained. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared method to produce anisotropy-corrected Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) satellite imagers. Regional and diurnal changes in model land surface temperature (LST) performance can be assessed owing to the somewhat continuous measurements of the LST offered by GEO satellites - measurements which are accurate to within 0.2 K. A seasonal, hourly comparison of satellite-observed LST with the NASA Goddard Earth Observing System Version 5 (GEOS-5) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) LST estimates is conducted to reveal regional and diurnal biases. This assessment is an important first step for evaluating the effectiveness of Ts assimilation, as well for determining the

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.

Looking on the bright side: biased attention and the human serotonin transporter gene

OpenAIRE

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-01-01

Humans differ in terms of biased attention for emotional stimuli and these biases can confer differential resilience and vulnerability to emotional disorders. Selective processing of positive emotional information, for example, is associated with enhanced sociability and well-being while a bias for negative material is associated with neuroticism and anxiety. A tendency to selectively avoid negative material might also be associated with mental health and well-being. The neurobiological mecha...

A biased competition account of attention and memory in Alzheimer's disease.

Science.gov (United States)

Finke, Kathrin; Myers, Nicholas; Bublak, Peter; Sorg, Christian

2013-10-19

The common view of Alzheimer's disease (AD) is that of an age-related memory disorder, i.e. declarative memory deficits are the first signs of the disease and associated with progressive brain changes in the medial temporal lobes and the default mode network. However, two findings challenge this view. First, new model-based tools of attention research have revealed that impaired selective attention accompanies memory deficits from early pre-dementia AD stages on. Second, very early distributed lesions of lateral parietal networks may cause these attention deficits by disrupting brain mechanisms underlying attentional biased competition. We suggest that memory and attention impairments might indicate disturbances of a common underlying neurocognitive mechanism. We propose a unifying account of impaired neural interactions within and across brain networks involved in attention and memory inspired by the biased competition principle. We specify this account at two levels of analysis: at the computational level, the selective competition of representations during both perception and memory is biased by AD-induced lesions; at the large-scale brain level, integration within and across intrinsic brain networks, which overlap in parietal and temporal lobes, is disrupted. This account integrates a large amount of previously unrelated findings of changed behaviour and brain networks and favours a brain mechanism-centred view on AD.

When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias

OpenAIRE

Trippas, Dries; Thompson, Valerie A.; Handley, Simon J.

2016-01-01

Two experiments pitted the default-interventionist account of belief bias against a parallel-processing model. According to the former, belief bias occurs because a fast, belief-based evaluation of the conclusion pre-empts a working-memory demanding logical analysis. In contrast, according to the latter both belief-based and logic-based responding occur in parallel. Participants were given deductive reasoning problems of variable complexity and instructed to decide whether the conclusion was ...

Potential sources of analytical bias and error in selected trace element data-quality analyses

Science.gov (United States)

Paul, Angela P.; Garbarino, John R.; Olsen, Lisa D.; Rosen, Michael R.; Mebane, Christopher A.; Struzeski, Tedmund M.

2016-09-28

Potential sources of analytical bias and error associated with laboratory analyses for selected trace elements where concentrations were greater in filtered samples than in paired unfiltered samples were evaluated by U.S. Geological Survey (USGS) Water Quality Specialists in collaboration with the USGS National Water Quality Laboratory (NWQL) and the Branch of Quality Systems (BQS).Causes for trace-element concentrations in filtered samples to exceed those in associated unfiltered samples have been attributed to variability in analytical measurements, analytical bias, sample contamination either in the field or laboratory, and (or) sample-matrix chemistry. These issues have not only been attributed to data generated by the USGS NWQL but have been observed in data generated by other laboratories. This study continues the evaluation of potential analytical bias and error resulting from matrix chemistry and instrument variability by evaluating the performance of seven selected trace elements in paired filtered and unfiltered surface-water and groundwater samples collected from 23 sampling sites of varying chemistries from six States, matrix spike recoveries, and standard reference materials.Filtered and unfiltered samples have been routinely analyzed on separate inductively coupled plasma-mass spectrometry instruments. Unfiltered samples are treated with hydrochloric acid (HCl) during an in-bottle digestion procedure; filtered samples are not routinely treated with HCl as part of the laboratory analytical procedure. To evaluate the influence of HCl on different sample matrices, an aliquot of the filtered samples was treated with HCl. The addition of HCl did little to differentiate the analytical results between filtered samples treated with HCl from those samples left untreated; however, there was a small, but noticeable, decrease in the number of instances where a particular trace-element concentration was greater in a filtered sample than in the associated

Alleviating tropical Atlantic sector biases in the Kiel climate model by enhancing horizontal and vertical atmosphere model resolution: climatology and interannual variability

Science.gov (United States)

Harlaß, Jan; Latif, Mojib; Park, Wonsun

2018-04-01

We investigate the quality of simulating tropical Atlantic (TA) sector climatology and interannual variability in integrations of the Kiel climate model (KCM) with varying atmosphere model resolution. The ocean model resolution is kept fixed. A reasonable simulation of TA sector annual-mean climate, seasonal cycle and interannual variability can only be achieved at sufficiently high horizontal and vertical atmospheric resolution. Two major reasons for the improvements are identified. First, the western equatorial Atlantic westerly surface wind bias in spring can be largely eliminated, which is explained by a better representation of meridional and especially vertical zonal momentum transport. The enhanced atmospheric circulation along the equator in turn greatly improves the thermal structure of the upper equatorial Atlantic with much reduced warm sea surface temperature (SST) biases. Second, the coastline in the southeastern TA and steep orography are better resolved at high resolution, which improves wind structure and in turn reduces warm SST biases in the Benguela upwelling region. The strongly diminished wind and SST biases at high atmosphere model resolution allow for a more realistic latitudinal position of the intertropical convergence zone. Resulting stronger cross-equatorial winds, in conjunction with a shallower thermocline, enable a rapid cold tongue development in the eastern TA in boreal spring. This enables simulation of realistic interannual SST variability and its seasonal phase locking in the KCM, which primarily is the result of a stronger thermocline feedback. Our findings suggest that enhanced atmospheric resolution, both vertical and horizontal, could be a key to achieving more realistic simulation of TA climatology and interannual variability in climate models.

A Realization of Bias Correction Method in the GMAO Coupled System

Science.gov (United States)

Chang, Yehui; Koster, Randal; Wang, Hailan; Schubert, Siegfried; Suarez, Max

2018-01-01

Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.

Codon usage bias analysis for the coding sequences of Camellia ...

African Journals Online (AJOL)

sunny t

2016-02-24

Feb 24, 2016 ... suggested that codon usage bias is driven by selection, particularly for .... For example, as mentioned above, highly expressed genes tend to use fewer ... directional codon bias measure effective number of codons (ENc) was ...

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...

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.

Temperature effects on pitfall catches of epigeal arthropods: a model and method for bias correction.

Science.gov (United States)

Saska, Pavel; van der Werf, Wopke; Hemerik, Lia; Luff, Martin L; Hatten, Timothy D; Honek, Alois; Pocock, Michael

2013-02-01

Carabids and other epigeal arthropods make important contributions to biodiversity, food webs and biocontrol of invertebrate pests and weeds. Pitfall trapping is widely used for sampling carabid populations, but this technique yields biased estimates of abundance ('activity-density') because individual activity - which is affected by climatic factors - affects the rate of catch. To date, the impact of temperature on pitfall catches, while suspected to be large, has not been quantified, and no method is available to account for it. This lack of knowledge and the unavailability of a method for bias correction affect the confidence that can be placed on results of ecological field studies based on pitfall data.Here, we develop a simple model for the effect of temperature, assuming a constant proportional change in the rate of catch per °C change in temperature, r , consistent with an exponential Q 10 response to temperature. We fit this model to 38 time series of pitfall catches and accompanying temperature records from the literature, using first differences and other detrending methods to account for seasonality. We use meta-analysis to assess consistency of the estimated parameter r among studies.The mean rate of increase in total catch across data sets was 0·0863 ± 0·0058 per °C of maximum temperature and 0·0497 ± 0·0107 per °C of minimum temperature. Multiple regression analyses of 19 data sets showed that temperature is the key climatic variable affecting total catch. Relationships between temperature and catch were also identified at species level. Correction for temperature bias had substantial effects on seasonal trends of carabid catches. Synthesis and Applications . The effect of temperature on pitfall catches is shown here to be substantial and worthy of consideration when interpreting results of pitfall trapping. The exponential model can be used both for effect estimation and for bias correction of observed data. Correcting for temperature

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.

[Study on correction of data bias caused by different missing mechanisms in survey of medical expenditure among students enrolling in Urban Resident Basic Medical Insurance].

Science.gov (United States)

Zhang, Haixia; Zhao, Junkang; Gu, Caijiao; Cui, Yan; Rong, Huiying; Meng, Fanlong; Wang, Tong

2015-05-01

The study of the medical expenditure and its influencing factors among the students enrolling in Urban Resident Basic Medical Insurance (URBMI) in Taiyuan indicated that non response bias and selection bias coexist in dependent variable of the survey data. Unlike previous studies only focused on one missing mechanism, a two-stage method to deal with two missing mechanisms simultaneously was suggested in this study, combining multiple imputation with sample selection model. A total of 1 190 questionnaires were returned by the students (or their parents) selected in child care settings, schools and universities in Taiyuan by stratified cluster random sampling in 2012. In the returned questionnaires, 2.52% existed not missing at random (NMAR) of dependent variable and 7.14% existed missing at random (MAR) of dependent variable. First, multiple imputation was conducted for MAR by using completed data, then sample selection model was used to correct NMAR in multiple imputation, and a multi influencing factor analysis model was established. Based on 1 000 times resampling, the best scheme of filling the random missing values is the predictive mean matching (PMM) method under the missing proportion. With this optimal scheme, a two stage survey was conducted. Finally, it was found that the influencing factors on annual medical expenditure among the students enrolling in URBMI in Taiyuan included population group, annual household gross income, affordability of medical insurance expenditure, chronic disease, seeking medical care in hospital, seeking medical care in community health center or private clinic, hospitalization, hospitalization canceled due to certain reason, self medication and acceptable proportion of self-paid medical expenditure. The two-stage method combining multiple imputation with sample selection model can deal with non response bias and selection bias effectively in dependent variable of the survey data.

Thermodynamic constitutive model for load-biased thermal cycling test of shape memory alloy

International Nuclear Information System (INIS)

Young, Sung; Nam, Tae-Hyun

2013-01-01

Graphical abstract: - Highlights: • Thermodynamic calculation model for martensitic transformation of shape memory alloy was proposed. • Evolution of the self-accommodation was considered independently by a rate-dependent kinetic equation. • Finite element calculation was conducted for B2–B19′ transformation of Ti–44.5Ni–5Cu–0.5 V (at.%). • Three-dimensional numerical results predict the macroscopic strain under bias loading accurately. - Abstract: This paper presents a three-dimensional calculation model for martensitic phase transformation of shape memory alloy. Constitutive model based on thermodynamic theory was provided. The average behavior was accounted for by considering the volume fraction of each martensitic variant in the material. Evolution of the volume fraction of each variant was determined by a rate-dependent kinetic equation. We assumed that nucleation rate is faster for the self-accommodation than for the stress-induced variants. Three-dimensional finite element analysis was conducted and the results were compared with the experimental data of Ti–44.5Ni–5Cu–0.5 V (at.%) alloy under bias loading

Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

Directory of Open Access Journals (Sweden)

Claire A Hales

Full Text Available Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142, and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

Science.gov (United States)

Hales, Claire A; Robinson, Emma S J; Houghton, Conor J

2016-01-01

Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142), and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

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.

Longitudinal Biases in the Seychelles Dome Simulated by 34 Ocean-Atmosphere Coupled General Circulation Models

Science.gov (United States)

Nagura, M.; Sasaki, W.; Tozuka, T.; Luo, J.; Behera, S. K.; Yamagata, T.

2012-12-01

The upwelling dome of the southern tropical Indian Ocean is examined by using simulated results from 34 ocean-atmosphere coupled general circulation models (CGCMs) including those from the phase five of the Coupled Model Intercomparison Project (CMIP5). Among the current set of the 34 CGCMs, 12 models erroneously produce the upwelling dome in the eastern half of the basin while the observed Seychelles Dome is located in the southwestern tropical Indian Ocean (Figure 1). The annual mean Ekman pumping velocity is almost zero in the southern off-equatorial region in these models. This is in contrast with the observations that show Ekman upwelling as the cause of the Seychelles Dome. In the models that produce the dome in the eastern basin, the easterly biases are prominent along the equator in boreal summer and fall that cause shallow thermocline biases along the Java and Sumatra coasts via Kelvin wave dynamics and result in a spurious upwelling dome there. In addition, these models tend to overestimate (underestimate) the magnitude of annual (semiannual) cycle of thermocline depth variability in the dome region, which is another consequence of the easterly wind biases in boreal summer-fall. Compared to the CMIP3 models (Yokoi et al. 2009), the CMIP5 models are even worse in simulating the dome longitudes and magnitudes of annual and semiannual cycles of thermocline depth variability in the dome region. Considering the increasing need to understand regional impacts of climate modes, these results may give serious caveats to interpretation of model results and help in further model developments.; Figure 1: The longitudes of the shallowest annual-mean D20 in 5°S-12°S. The open and filled circles are for the observations and the CGCMs, respectively.

A note on exponential dispersion models which are invariant under length-biased sampling

NARCIS (Netherlands)

Bar-Lev, S.K.; van der Duyn Schouten, F.A.

2003-01-01

Length-biased sampling situations may occur in clinical trials, reliability, queueing models, survival analysis and population studies where a proper sampling frame is absent.In such situations items are sampled at rate proportional to their length so that larger values of the quantity being

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.

W(h)ither the Oracle? Cognitive biases and other human challenges of integrated environmental modeling

Science.gov (United States)

Glynn, Pierre D.; Ames, D.P.; Quinn, N. W. T.; Rizzoli, A.E.

2014-01-01

Integrated environmental modeling (IEM) can organize and increase our knowledge of the complex, dynamic ecosystems that house our natural resources and control the quality of our environments. Human behavior, however, must be taken into account. Human biases/heuristics reflect adaptation over our evolutionary past to frequently experienced situations that affected our survival and that provided sharply distinguished feedbacks at the level of the individual. Unfortunately, human behavior is not adapted to the more diffusely experienced, less frequently encountered, problems and issues that IEM typically seeks to address in the simulation of natural resources and environments. While seeking inspiration from the prophetic traditions of the Oracle of Delphi, several human biases are identified that may affect how the science base of IEM is assembled, and how IEM results are interpreted and used. These biases are supported by personal observations, and by the findings of behavioral scientists. A process for critical analysis is proposed that solicits explicit accounting and cognizance of potential human biases. A number of suggestions are made to address the human challenges of IEM, in addition to maintaining attitudes of watchful humility, open-mindedness, honesty, and transparent accountability. These include creating a new area of study in the behavioral biogeosciences, using structured processes for engaging the modeling and stakeholder community in IEM, and using “red teams” to increase resilience of IEM constructs and use.

Media Bias and Advertising: Evidence from German Car Magazines

OpenAIRE

Dewenter, Ralf; Heimeshoff, Ulrich

2011-01-01

This paper investigates the existence of a possible media bias by analyzing the impact of automobile manufactures' advertisements on automobile reviews in German car magazines. By accounting for both endogeneity and sample selection we find a positive impact of advertising volumes on test scores. Moreover, also a home bias in terms of higher scores for German cars is observable. We account these results as some evidence for a media bias, induced by the two-sidedness of the markets.

Do Methodological Choices in Environmental Modeling Bias Rebound Effects? A Case Study on Electric Cars.

Science.gov (United States)

Font Vivanco, David; Tukker, Arnold; Kemp, René

2016-10-18

Improvements in resource efficiency often underperform because of rebound effects. Calculations of the size of rebound effects are subject to various types of bias, among which methodological choices have received particular attention. Modellers have primarily focused on choices related to changes in demand, however, choices related to modeling the environmental burdens from such changes have received less attention. In this study, we analyze choices in the environmental assessment methods (life cycle assessment (LCA) and hybrid LCA) and environmental input-output databases (E3IOT, Exiobase and WIOD) used as a source of bias. The analysis is done for a case study on battery electric and hydrogen cars in Europe. The results describe moderate rebound effects for both technologies in the short term. Additionally, long-run scenarios are calculated by simulating the total cost of ownership, which describe notable rebound effect sizes-from 26 to 59% and from 18 to 28%, respectively, depending on the methodological choices-with favorable economic conditions. Relevant sources of bias are found to be related to incomplete background systems, technology assumptions and sectorial aggregation. These findings highlight the importance of the method setup and of sensitivity analyses of choices related to environmental modeling in rebound effect assessments.

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.

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

Science.gov (United States)

Van Weverberg, K.; Morcrette, C. J.; Petch, J.; Klein, S. A.; Ma, H.-Y.; Zhang, C.; Xie, S.; Tang, Q.; Gustafson, W. I.; Qian, Y.; Berg, L. K.; Liu, Y.; Huang, M.; Ahlgrimm, M.; Forbes, R.; Bazile, E.; Roehrig, R.; Cole, J.; Merryfield, W.; Lee, W.-S.; Cheruy, F.; Mellul, L.; Wang, Y.-C.; Johnson, K.; Thieman, M. M.

2018-04-01

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

[Attentional bias and emotional suppression in borderline personality disorder].

Science.gov (United States)

Fernando, Silvia Carvalho; Griepenstroh, Julia; Urban, Sabine; Driessen, Martin; Beblo, Thomas

2014-01-01

Emotion regulation dysfunctions marked by negative affectivity are a core feature of borderline personality disorder (BPD). In addition, patients with BPD show disturbed attentional processes which become particularly apparent in the domain of selective attention when emotional stimuli are presented (negative attentional bias). Assuming that emotion regulation is linked to attentional deployment processes, this study aimed (1) to determine whether a negative attentional bias is established by using film clips of fearful faces and (2) to investigate the association between dysfunctional emotion regulation strategies (emotional suppression) and negative attention bias in BPD. We investigated 18 inpatients with BPD and 18 healthy control participants using the modified version of the fearful face-paradigm to assess the inhibition of emotional stimuli. We also administered self-report emotion regulation questionnaires. Compared to the healthy controls, patients with BPD showed significant longer reaction times during the emotional versus the neutral film stimuli in the modified fearful face-paradigm. With regard to the second hypothesis, we failed to find an association between the negative attentional bias and the habitual use of emotional suppression in BPD. In this study, we could confirm an attentional bias for negative stimuli, using complex, dynamic material. Future studies need to address the impact of confounding variables (e. g. comorbid disorders) on the relationship between maladaptive emotion regulation and selective attentional bias.

Attention processes in chronic fatigue syndrome: attentional bias for health-related threat and the role of attentional control.

Science.gov (United States)

Hou, Ruihua; Moss-Morris, Rona; Risdale, Anna; Lynch, Jeannette; Jeevaratnam, Preshan; Bradley, Brendan P; Mogg, Karin

2014-01-01

Cognitive behavioural models of chronic fatigue syndrome (CFS) propose that attention processes, specifically, enhanced selective attention to health-threat related cues, may play an important role in symptom maintenance. The current study investigated attentional bias towards health-threat stimuli in CFS. It also examined whether individuals with CFS have impaired executive attention, and whether this was related to attentional bias. 27 participants with CFS and 35 healthy controls completed a Visual Probe Task measuring attentional bias, and an Attention Network Test measuring executive attention, alerting and orienting. Participants also completed self-report measures of CFS and mood symptoms. Compared to the control group, the CFS group showed greater attentional bias for health-threat words than pictures; and the CFS group was significantly impaired in executive attention. Furthermore, CFS individuals with poor executive attention showed greater attentional bias to health-threat related words, compared not only to controls but also to CFS individuals with good executive attention. Thus, this study revealed a significant relationship between attentional bias and executive attention in CFS: attentional bias to threat was primarily evident in those with impaired executive attention control. Taking account of individual differences in executive attention control in current intervention models may be beneficial for CFS. Copyright © 2013 Elsevier Ltd. All rights reserved.

Scale dependence of the halo bias in general local-type non-Gaussian models I: analytical predictions and consistency relations

International Nuclear Information System (INIS)

Nishimichi, Takahiro

2012-01-01

The large-scale clustering pattern of biased tracers is known to be a powerful probe of the non-Gaussianities in the primordial fluctuations. The so-called scale-dependent bias has been reported in various type of models of primordial non-Gaussianities. We focus on local-type non-Gaussianities, and unify the derivations in the literature of the scale-dependent bias in the presence of multiple Gaussian source fields as well as higher-order coupling to cover the models described by frequently-discussed f NL , g NL and t NL parameterization. We find that the resultant power spectrum is characterized by two parameters responsible for the shape and the amplitude of the scale-dependent bias in addition to the Gaussian bias factor. We show how (a generalized version of) Suyama-Yamaguchi inequality between f NL and t NL can directly be accessible from the observed power spectrum through the dependence on our new parameter which controls the shape of the scale-dependent bias. The other parameter for the amplitude of the scale-dependent bias is shown to be useful to distinguish the simplest quadratic non-Gaussianities (i.e., f NL -type) from higher-order ones (g NL and higher), if one measures it from multiple species of galaxies or clusters of galaxies. We discuss the validity and limitations of our analytic results by comparison with numerical simulations in an accompanying paper

Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

Science.gov (United States)

Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai

2017-09-01

Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

Bias, accuracy and impact of indirect genetic effects in infectious diseases

Directory of Open Access Journals (Sweden)

Debby eLipschutz-Powell

2012-10-01

Full Text Available Selection for improved host response to infectious disease offers a desirable alternative to chemical treatment but has proven difficult in practice, due to low heritability estimates of disease traits. Disease data from field studies is often binary, indicating whether an individual has become infected or not following exposure to an infectious disease. Numerous studies have shown that from this data one can infer genetic variation in individuals’ underlying susceptibility. In a previous study, we showed that with an Indirect Genetic Effect (IGE model it is possible to capture some genetic variation in infectivity, if present, as well as in susceptibility. Infectivity is the propensity of transmitting infection upon contact with a susceptible individual. It is an important factor determining the severity of an epidemic. However, there are severe shortcomings with the Standard IGE models as they do not accommodate the dynamic nature of disease data. Here we adjust the Standard IGE model to (1 make expression of infectivity dependent on the individuals’ disease status (Case Model and (2 to include timing of infection (Case-ordered Model. The models are evaluated by comparing impact of selection, bias and accuracy of each model using simulated binary disease data. These were generated for populations with known variation in susceptibility and infectivity thus allowing comparisons between estimated and true breeding values. Overall the Case Model provided better estimates for host genetic susceptibility and infectivity compared to the Standard Model in terms of bias, impact and accuracy. Furthermore, these estimates were strongly influenced by epidemiological characteristics. However, surprisingly, the Case Ordered model performed considerably worse than the Standard and the Case Models, pointing towards limitations in incorporating disease dynamics into conventional variance component estimation methodology and software used in animal breeding.

Competing risk bias was common in Kaplan-Meier risk estimates published in prominent medical journals.

Science.gov (United States)

van Walraven, Carl; McAlister, Finlay A

2016-01-01

Risk estimates from Kaplan-Meier curves are well known to medical researchers, reviewers, and editors. In this study, we determined the proportion of Kaplan-Meier analyses published in prominent medical journals that are potentially biased because of competing events ("competing risk bias"). We randomly selected 100 studies that had at least one Kaplan-Meier analysis and were recently published in prominent medical journals. Susceptibility to competing risk bias was determined by examining the outcome and potential competing events. In susceptible studies, bias was quantified using a previously validated prediction model when the number of outcomes and competing events were given. Forty-six studies (46%) contained Kaplan-Meier analyses susceptible to competing risk bias. Sixteen studies (34.8%) susceptible to competing risk cited the number of outcomes and competing events; in six of these studies (6/16, 37.5%), the outcome risk from the Kaplan-Meier estimate (relative to the true risk) was biased upward by 10% or more. Almost half of Kaplan-Meier analyses published in medical journals are susceptible to competing risk bias and may overestimate event risk. This bias was found to be quantitatively important in a third of such studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Adjusting multistate capture-recapture models for misclassification bias: manatee breeding proportions

Science.gov (United States)

Kendall, W.L.; Hines, J.E.; Nichols, J.D.

2003-01-01

Matrix population models are important tools for research and management of populations. Estimating the parameters of these models is an important step in applying them to real populations. Multistate capture-recapture methods have provided a useful means for estimating survival and parameters of transition between locations or life history states but have mostly relied on the assumption that the state occupied by each detected animal is known with certainty. Nevertheless, in some cases animals can be misclassified. Using multiple capture sessions within each period of interest, we developed a method that adjusts estimates of transition probabilities for bias due to misclassification. We applied this method to 10 years of sighting data for a population of Florida manatees (Trichechus manatus latirostris) in order to estimate the annual probability of transition from nonbreeding to breeding status. Some sighted females were unequivocally classified as breeders because they were clearly accompanied by a first-year calf. The remainder were classified, sometimes erroneously, as nonbreeders because an attendant first-year calf was not observed or was classified as more than one year old. We estimated a conditional breeding probability of 0.31 + 0.04 (estimate + 1 SE) when we ignored misclassification bias, and 0.61 + 0.09 when we accounted for misclassification.

Understanding the effect of an excessive cold tongue bias on projecting the tropical Pacific SST warming pattern in CMIP5 models

Science.gov (United States)

Ying, Jun; Huang, Ping; Lian, Tao; Tan, Hongjian

2018-05-01

An excessive cold tongue is a common bias among current climate models, and considered an important source of bias in projections of tropical Pacific climate change under global warming. Specifically, the excessive cold tongue bias is closely related to the tropical Pacific SST warming (TPSW) pattern. In this study, we reveal that two processes are the critical mechanisms by which the excessive cold tongue bias influences the projection of the TPSW pattern, based on 32 models from phase 5 of Coupled Model Intercomparison Projection (CMIP5). On the one hand, by assuming that the shortwave (SW) radiation to SST feedback is linearly correlated to the cold tongue SST, the excessive cold tongue bias can induce an overly weak negative SW-SST feedback in the central Pacific, which can lead to a positive SST warming bias in the central to western Pacific (around 150°E-140°W). Moreover, the overly weak local atmospheric dynamics response to SST is a key process of the overly weak SW-SST feedback, compared with the cloud response to atmospheric dynamics and the SW radiation response to cloud. On the other hand, the overly strong ocean zonal overturning circulation associated with the excessive cold tongue bias results in an overestimation of the ocean dynamical thermostat effect, with enhanced ocean stratification under global warming, leading to a negative SST warming bias in the central and eastern Pacific (around 170°W-120°W). These two processes jointly form a positive SST warming bias in the western Pacific, contributing to a La Niña-like warming bias. Therefore, we suggest a more realistic climatological cold tongue SST is needed for a more reliable projection of the TPSW pattern.

Model under-representation of decadal Pacific trade wind trends and its link to tropical Atlantic bias

Science.gov (United States)

Kajtar, Jules B.; Santoso, Agus; McGregor, Shayne; England, Matthew H.; Baillie, Zak

2018-02-01

The strengthening of the Pacific trade winds in recent decades has been unmatched in the observational record stretching back to the early twentieth century. This wind strengthening has been connected with numerous climate-related phenomena, including accelerated sea-level rise in the western Pacific, alterations to Indo-Pacific ocean currents, increased ocean heat uptake, and a slow-down in the rate of global-mean surface warming. Here we show that models in the Coupled Model Intercomparison Project phase 5 underestimate the observed range of decadal trends in the Pacific trade winds, despite capturing the range in decadal sea surface temperature (SST) variability. Analysis of observational data suggests that tropical Atlantic SST contributes considerably to the Pacific trade wind trends, whereas the Atlantic feedback in coupled models is muted. Atmosphere-only simulations forced by observed SST are capable of recovering the time-variation and the magnitude of the trade wind trends. Hence, we explore whether it is the biases in the mean or in the anomalous SST patterns that are responsible for the under-representation in fully coupled models. Over interannual time-scales, we find that model biases in the patterns of Atlantic SST anomalies are the strongest source of error in the precipitation and atmospheric circulation response. In contrast, on decadal time-scales, the magnitude of the model biases in Atlantic mean SST are directly linked with the trade wind variability response.

Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias

International Nuclear Information System (INIS)

Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Adams, Paul D.; Read, Randy J.; Zwart, Peter H.; Hung, Li-Wei

2008-01-01

An OMIT procedure is presented that has the benefits of iterative model building density modification and refinement yet is essentially unbiased by the atomic model that is built. A procedure for carrying out iterative model building, density modification and refinement is presented in which the density in an OMIT region is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite ‘iterative-build’ OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular-replacement structure and with an experimentally phased structure and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank

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.

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.

Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

Science.gov (United States)

Jarrell, Stephen B.; Stanley, T. D.

2004-01-01

The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

Reducing biases on H0 measurements using strong lensing and galaxy dynamics: results from the EAGLE simulation

Science.gov (United States)

Tagore, Amitpal S.; Barnes, David J.; Jackson, Neal; Kay, Scott T.; Schaller, Matthieu; Schaye, Joop; Theuns, Tom

2018-03-01

Cosmological parameter constraints from observations of time-delay lenses are becoming increasingly precise. However, there may be significant bias and scatter in these measurements due to, among other things, the so-called mass-sheet degeneracy. To estimate these uncertainties, we analyse strong lenses from the largest EAGLE hydrodynamical simulation. We apply a mass-sheet transformation to the radial density profiles of lenses, and by selecting lenses near isothermality, we find that the bias on H0 can be reduced to 5 per cent with an intrinsic scatter of 10 per cent, confirming previous results performed on a different simulation data set. We further investigate whether combining lensing observables with kinematic constraints helps to minimize this bias. We do not detect any significant dependence of the bias on lens model parameters or observational properties of the galaxy, but depending on the source-lens configuration, a bias may still exist. Cross lenses provide an accurate estimate of the Hubble constant, while fold (double) lenses tend to be biased low (high). With kinematic constraints, double lenses show bias and intrinsic scatter of 6 per cent and 10 per cent, respectively, while quad lenses show bias and intrinsic scatter of 0.5 per cent and 10 per cent, respectively. For lenses with a reduced χ2 > 1, a power-law dependence of the χ2 on the lens environment (number of nearby galaxies) is seen. Lastly, we model, in greater detail, the cases of two double lenses that are significantly biased. We are able to remove the bias, suggesting that the remaining biases could also be reduced by carefully taking into account additional sources of systematic uncertainty.

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.

Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

Science.gov (United States)

O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

2015-04-01

Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

Biases in cost measurement for economic evaluation studies in health care.

Science.gov (United States)

Jacobs, P; Baladi, J F

1996-01-01

This paper addresses the issue of biases in cost measures which used in economic evaluation studies. The basic measure of hospital costs which is used by most investigators is unit cost. Focusing on this measure, a set of criteria which the basic measures must fulfil in order to approximate the marginal cost (MC) of a service for the relevant product, in the representative site, was identified. Then four distinct biases--a scale bias, a case mix bias, a methods bias and a site selection bias--each of which reflects the divergence of the unit cost measure from the desired MC measure, were identified. Measures are proposed for several of these biases and it is suggested how they can be corrected.

Impact of changes in the formulation of cloud-related processes on model biases and climate feedbacks

NARCIS (Netherlands)

Lacagnina, C.; Selten, F.; Siebesma, A.P.

2014-01-01

To test the impact of modeling uncertainties and biases on the simulation of cloud feedbacks, several configurations of the EC-Earth climate model are built altering physical parameterizations. An overview of the various radiative feedbacks diagnosed from the reference EC-Earth configuration is

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.

Measurement bias detection with Kronecker product restricted models for multivariate longitudinal data: an illustration with health-related quality of life data from thirteen measurement occasions.

Science.gov (United States)

Verdam, Mathilde G E; Oort, Frans J

2014-01-01

Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data.A method for the investigation of measurement bias with Kronecker product restricted models.Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions.The use of curves to facilitate substantive interpretation of apparent measurement bias.Assessment of change in common factor means, after accounting for apparent measurement bias.Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks.

Regularity of the Speed of Biased Random Walk in a One-Dimensional Percolation Model

Science.gov (United States)

Gantert, Nina; Meiners, Matthias; Müller, Sebastian

2018-03-01

We consider biased random walks on the infinite cluster of a conditional bond percolation model on the infinite ladder graph. Axelson-Fisk and Häggström established for this model a phase transition for the asymptotic linear speed \\overline{v} of the walk. Namely, there exists some critical value λ c>0 such that \\overline{v}>0 if λ \\in (0,λ c) and \\overline{v}=0 if λ ≥ λ c. We show that the speed \\overline{v} is continuous in λ on (0,∞) and differentiable on (0,λ c/2). Moreover, we characterize the derivative as a covariance. For the proof of the differentiability of \\overline{v} on (0,λ c/2), we require and prove a central limit theorem for the biased random walk. Additionally, we prove that the central limit theorem fails to hold for λ ≥ λ c/2.

Correcting for non-response bias in contingent valuation surveys concerning environmental non-market goods

DEFF Research Database (Denmark)

Bonnichsen, Ole; Olsen, Søren Bøye

2016-01-01

Data collection for economic valuation by using Internet surveys and pre-recruited Internet panels can be associated with severe disadvantages. Problems concerning sample coverage and sample representativeness can be expected. Representation errors may occur since people can choose whether....... This paper analyses a sample used for an Internet contingent valuation method survey eliciting preferences for improvements in water quality of a river. We find that some variables that affect the survey participation decision also affect willingness-to-pay, consequently biasing our welfare estimates. We...... show how adjusting willingness-to-pay for this bias can be accomplished by using a grouped data model incorporating a correlation parameter to account for selection....

The applicability of fair selection models in the South African context

Directory of Open Access Journals (Sweden)

G. K. Huysamen

1995-06-01

Full Text Available This article reviews several models that are aimed at achieving fair selection in situations in which underrepresented groups tend to obtain lower scores on selection tests. Whereas predictive bias is a statistical concept that refers to systematic errors in the prediction of individuals' criterion scores, selection fairness pertains to the extent to which selection results meet certain socio-political demands. The regression and equal-risk models adjust for differences in the criterion-on-test regression lines of different groups. The constant ratio, conditional probability and equal probability models manipulate the test cutoff scores of different groups so that certain ratios formed between different selection outcomes (correct acceptances, correct rejections, incorrect acceptances, incorrect rejections are the same for such groups. The decision-theoretic approach requires that utilities be attached to these different outcomes for different groups. These procedures are not only eminently suited to accommodate calls for affirmative action, but they also serve the cause of transparency. Opsomming Hierdie artikel verskaf 'n oorsig van verskeie modelle om billike keuring te verkry in situasies waar onderverteen-woordigende groepe geneig is om swakker op keuringstoetse te vaar. Terwyl voorspellingsydigheid 'n statistiese begrip is wat betrekking het op stelselmatige foute in die voorspelling van individue se kriteriumtellings, het keuringsbillikheid te make met die mate waarin keuringsresultate aan sekere sosiaal-politieke vereistes voldoen. Die regressieen gelyke-risiko-modelle maak aanpassings vir verskille in die kriterium-op-toetsregressielyne van verskillende groepe. Die konstante-verhoudings, voorwaardelike-waarskynlikheids- en gelyke-waarskynlikheidsmodelle manipuleer die toetsafkappunte van verskillende groepe sodat sekere verhoudings wat tussen keuringsresultate (korrekte aanvaardings, verkeerde aanvaardings, korrekte verwerpings

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.

Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts

DEFF Research Database (Denmark)

Seaby, Lauren Paige; Refsgaard, Jens Christian; Sonnenborg, Torben

2015-01-01

Model pairing, this paper analyses the relationship between complexity and robustness of three distribution-based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty......The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate...... signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological...

Towards a new model of attentional biases in the development, maintenance, and management of pain.

Science.gov (United States)

Todd, Jemma; Sharpe, Louise; Johnson, Ameika; Nicholson Perry, Kathryn; Colagiuri, Ben; Dear, Blake F

2015-09-01

Individuals with chronic pain demonstrate attentional biases (ABs) towards pain-related stimuli. However, the clinical importance of these biases is yet to be determined and a sound theoretical model for explaining the role of ABs in the development and maintenance of pain is lacking. Within this article, we (1) systematically review prospective and experimental research exploring ABs and pain outcomes in light of current theoretical models and (2) propose a theoretical framework for understanding AB in pain. Across prospective research, an attentional pattern of vigilance-avoidance was observed. Interventions targeting ABs were less consistent; however, there were promising findings among studies that found attentional training effects, particularly for laboratory research. The proposed Threat Interpretation Model suggests a relationship between threat, interpretation, and stimuli in determining attentional processes, which while tentative generates important testable predictions regarding the role of attention in pain and builds on previous theoretical and empirical work in this area.

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

Loss Aversion Reflects Information Accumulation, Not Bias: A Drift-Diffusion Model Study.

Science.gov (United States)

Clay, Summer N; Clithero, John A; Harris, Alison M; Reed, Catherine L

2017-01-01

Defined as increased sensitivity to losses, loss aversion is often conceptualized as a cognitive bias. However, findings that loss aversion has an attentional or emotional regulation component suggest that it may instead reflect differences in information processing. To distinguish these alternatives, we applied the drift-diffusion model (DDM) to choice and response time (RT) data in a card gambling task with unknown risk distributions. Loss aversion was measured separately for each participant. Dividing the participants into terciles based on loss aversion estimates, we found that the most loss-averse group showed a significantly lower drift rate than the other two groups, indicating overall slower uptake of information. In contrast, neither the starting bias nor the threshold separation (barrier) varied by group, suggesting that decision thresholds are not affected by loss aversion. These results shed new light on the cognitive mechanisms underlying loss aversion, consistent with an account based on information accumulation.

Minimizing the wintertime low bias of Northern Hemisphere carbon monoxide in global model simulations

Science.gov (United States)

Stein, Olaf; Schultz, Martin G.; Bouarar, Idir; Clark, Hannah; Huijnen, Vincent; Gaudel, Audrey; George, Maya; Clerbaux, Cathy

2015-04-01

Carbon monoxide (CO) is a product of incomplete combustion and is also produced from oxidation of volatile organic compounds (VOC) in the atmosphere. It is of interest as an indirect greenhouse gas and an air pollutant causing health effects and is thus subject to emission restrictions. CO acts as a major sink for the OH radical and as a precursor for tropospheric ozone and affects the oxidizing capacity of the atmosphere as well as regional air quality. Despite the developments in the global modelling of chemistry and of the parameterization of the physical processes, CO concentrations remain underestimated during NH winter by most state-of-the-art chemical transport models. The resulting model bias can in principle originate from either an underestimation of CO sources or an overestimation of its sinks. We address both the role of sources and sinks with a series of MOZART chemistry transport model sensitivity simulations for the year 2008 and compare our results to observational data from ground-based stations, satellite observations, and from MOZAIC tropospheric profile measurements on passenger aircraft. Our base case simulation using the MACCity emission inventory (Granier et al. 2011) underestimates the near-surface Northern Hemispheric CO mixing ratios by more than 20 ppb from December to April with a maximal bias of 40 ppb in January. The bias is strongest for the European region (up to 75 ppb in January). From our sensitivity studies the mismatch between observed and modelled atmospheric CO concentrations can be explained by a combination of the following emission inventory shortcuts: (i) missing anthropogenic wintertime CO emissions from traffic or other combustion processes, (ii) missing anthropogenic VOC emissions, (iii) an exaggerated downward trend in the RCP8.5 scenario underlying the MACCity inventory, (iv) a lack of knowledge about the seasonality of emissions. Deficiencies in the parameterization of the dry deposition velocities can also lead to

Investor’s Commitment Bias and Escalation of Firm’s Investment Decision

Directory of Open Access Journals (Sweden)

Anis JARBOUI

2012-12-01

Full Text Available This study examines the reasons of perseverance in firm’s investment decision. It shows the possible influence of three closely related features which are: firm’s financial indicators, investor’s risk profile, and investor’s commitment bias, on a firm’s investment decisions escalation. This study aims to provide evidence as to whether investor considers the financial and risk’s perception features (financial strength and risk profile to persevere his initial investment decision while he notes a high level of commitment bias. The proposed model of this paper uses GLM univariate data analyses to examine this relationship. Investor’s risk profile and his commitment bias have been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some 360 Tunisian individual investors. Our results have revealed that investors pay more attention to keep their psychology comfort than their financial comfort. It exposed the importance of the investor’s commitment bias and its risk perception in explaining investment decision escalation. Moreover results shows that there is strong and significant empirical relationship linking the escalatory behavior in investment decision and the interaction effects between the three independent variables. This means that, in practice, investors consider the three factors simultaneously.

Model selection in periodic autoregressions

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

1994-01-01

textabstractThis paper focuses on the issue of period autoagressive time series models (PAR) selection in practice. One aspect of model selection is the choice for the appropriate PAR order. This can be of interest for the valuation of economic models. Further, the appropriate PAR order is important

Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis.

Science.gov (United States)

Thompson, Jennifer A; Fielding, Katherine L; Davey, Calum; Aiken, Alexander M; Hargreaves, James R; Hayes, Richard J

2017-10-15

Many stepped wedge trials (SWTs) are analysed by using a mixed-effect model with a random intercept and fixed effects for the intervention and time periods (referred to here as the standard model). However, it is not known whether this model is robust to misspecification. We simulated SWTs with three groups of clusters and two time periods; one group received the intervention during the first period and two groups in the second period. We simulated period and intervention effects that were either common-to-all or varied-between clusters. Data were analysed with the standard model or with additional random effects for period effect or intervention effect. In a second simulation study, we explored the weight given to within-cluster comparisons by simulating a larger intervention effect in the group of the trial that experienced both the control and intervention conditions and applying the three analysis models described previously. Across 500 simulations, we computed bias and confidence interval coverage of the estimated intervention effect. We found up to 50% bias in intervention effect estimates when period or intervention effects varied between clusters and were treated as fixed effects in the analysis. All misspecified models showed undercoverage of 95% confidence intervals, particularly the standard model. A large weight was given to within-cluster comparisons in the standard model. In the SWTs simulated here, mixed-effect models were highly sensitive to departures from the model assumptions, which can be explained by the high dependence on within-cluster comparisons. Trialists should consider including a random effect for time period in their SWT analysis model. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Effects of acute dopamine precusor depletion on immediate reward selection bias and working memory depend on catechol-O-methyltransferase genotype.

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Kelm, Mary Katherine; Boettiger, Charlotte A

2013-12-01

Little agreement exists as to acute dopamine (DA) manipulation effects on intertemporal choice in humans. We previously found that catechol-O-methyltransferase (COMT) Val158Met genotype predicts individual differences in immediate reward selection bias among adults. Moreover, we and others have shown that the relationship between COMT genotype and immediate reward bias is inverted in adolescents. No previous pharmacology studies testing DA manipulation effects on intertemporal choice have accounted for COMT genotype, and many have included participants in the adolescent age range (18-21 years) as adults. Moreover, many studies have included female participants without strict cycle phase control, although recent evidence demonstrates that cyclic estradiol elevations interact with COMT genotype to affect DA-dependent cognition. These factors may have interacted with DA manipulations in past studies, potentially occluding detection of effects. Therefore, we predicted that, among healthy male adults (ages 22-40 years), frontal DA tone, as indexed by COMT genotype, would interact with acute changes in DA signaling to affect intertemporal choice. In a double-blind, placebo-controlled design, we decreased central DA via administration of an amino acid beverage deficient in the DA precursors, phenylalanine and tyrosine, and tested effects on immediate reward bias in a delay-discounting (DD) task and working memory (WM) in an n-back task. We found no main effect of beverage on DD or WM performance but did find significant beverage*genotype effects. These results suggest that the effect of DA manipulations on DD depends on individual differences in frontal DA tone, which may have impeded some past efforts to characterize DA's role in immediate reward bias in humans.

Measurement bias detection with Kronecker product restricted models for multivariate longitudinal data: an illustration with health-related quality of life data from thirteen measurement occasions

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Verdam, Mathilde G. E.; Oort, Frans J.

2014-01-01

Highlights Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data. A method for the investigation of measurement bias with Kronecker product restricted models. Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions. The use of curves to facilitate substantive interpretation of apparent measurement bias. Assessment of change in common factor means, after accounting for apparent measurement bias. Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks. PMID:25295016

Young children seek out biased information about social groups.

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Over, Harriet; Eggleston, Adam; Bell, Jenny; Dunham, Yarrow

2018-05-01

Understanding the origins of prejudice necessitates exploring the ways in which children participate in the construction of biased representations of social groups. We investigate whether young children actively seek out information that supports and extends their initial intergroup biases. In Studies 1 and 2, we show that children choose to hear a story that contains positive information about their own group and negative information about another group rather than a story that contains negative information about their own group and positive information about the other group. In a third study, we show that children choose to present biased information to others, thus demonstrating that the effects of information selection can start to propagate through social networks. In Studies 4 and 5, we further investigate the nature of children's selective information seeking and show that children prefer ingroup-favouring information to other types of biased information and even to balanced, unbiased information. Together, this work shows that children are not merely passively recipients of social information; they play an active role in the creation and transmission of intergroup attitudes. © 2017 John Wiley & Sons Ltd.

Sample selection may bias the outcome of an adolescent mental health survey: results from a five-year follow-up of 4171 adolescents.

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Kekkonen, V; Kivimäki, P; Valtonen, H; Hintikka, J; Tolmunen, T; Lehto, S M; Laukkanen, E

2015-02-01

The representativeness of the data is one of the main issues in evaluating the significance of research findings. Dropping out is common in adolescent mental health research, and may distort the results. Nevertheless, very little is known about the types of systematic bias that may affect studies in a) the informed consent phase and b) later in follow-up phases. The authors addressed this gap in knowledge in a five-year follow-up study on a sample of adolescents aged 13-18 years. The data were collected using self-report questionnaires. The baseline sample consisted of 4171 adolescents, 1827 (43.8%) of whom gave consent to be contacted for a follow-up survey, but only 797 (19.1%) participated in the follow-up. Binary logistic regression models were used to explain the participation. Young age, female gender, a high number of hobbies, good performance at school in the native language and general subjects, family disintegration such as divorce, high parental employment, and symptoms of depression and anxiety were associated with both consent and participation. However, the effect of mental health aspects was smaller than the effect of age and gender. This study confirmed the possibility of systematic selection bias by adolescents' sociodemographic characteristics. The representativeness of the study sample might have been improved by more intense recruitment strategies. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.