WorldWideScience

Sample records for model selection bias

  1. Selecting, weeding, and weighting biased climate model ensembles

    Science.gov (United States)

    Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.

    2012-12-01

    In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.

  2. Dealing with selection bias in educational transition models

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads Meier

    2011-01-01

    This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...

  3. Selection Bias in Educational Transition Models: Theory and Empirical Evidence

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads

    Most studies using Mare’s (1980, 1981) seminal model of educational transitions find that the effect of family background decreases across transitions. Recently, Cameron and Heckman (1998, 2001) have argued that the “waning coefficients” in the Mare model are driven by selection on unobserved...... the United States, United Kingdom, Denmark, and the Netherlands shows that when we take selection into account the effect of family background variables on educational transitions is largely constant across transitions. We also discuss several difficulties in estimating educational transition models which...

  4. Selection bias in species distribution models: An econometric approach on forest trees based on structural modeling

    Science.gov (United States)

    Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.

    2014-12-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents

  5. National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models

    Science.gov (United States)

    Hogan, Daniel R; Salomon, Joshua A; Canning, David; Hammitt, James K; Zaslavsky, Alan M; Bärnighausen, Till

    2012-01-01

    Objectives Population-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors. Methods For 12 Demographic and Health Surveys conducted from 2001 to 2009 (N=138 300), we predict HIV status among those missing a valid HIV test with Heckman-type selection models, which allow for correlation between infection status and participation in survey HIV testing. We compare these estimates with conventional ones and introduce a simulation procedure that incorporates regression model parameter uncertainty into confidence intervals. Results Selection model point estimates of national HIV prevalence were greater than unadjusted estimates for 10 of 12 surveys for men and 11 of 12 surveys for women, and were also greater than the majority of estimates obtained from conventional imputation, with significantly higher HIV prevalence estimates for men in Cote d'Ivoire 2005, Mali 2006 and Zambia 2007. Accounting for selective non-participation yielded 95% confidence intervals around HIV prevalence estimates that are wider than those obtained with conventional imputation by an average factor of 4.5. Conclusions Our analysis indicates that national HIV prevalence estimates for many countries in sub-Saharan African are more uncertain than previously thought, and may be underestimated in several cases, underscoring the need for increasing participation in HIV surveys. Heckman-type selection models should be included in the set of tools used for routine estimation of HIV prevalence. PMID:23172342

  6. Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models

    NARCIS (Netherlands)

    Reed, Thomas E; Gienapp, Phillip; Visser, Marcel E

    2016-01-01

    ey life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document micro-evolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have

  7. Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models

    NARCIS (Netherlands)

    Reed, Thomas E; Gienapp, Phillip; Visser, Marcel E

    2016-01-01

    Key life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document micro-evolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have

  8. Selfing in Haploid Plants and Efficacy of Selection: Codon Usage Bias in the Model Moss Physcomitrella patens.

    Science.gov (United States)

    Szövényi, Péter; Ullrich, Kristian K; Rensing, Stefan A; Lang, Daniel; van Gessel, Nico; Stenøien, Hans K; Conti, Elena; Reski, Ralf

    2017-06-01

    A long-term reduction in effective population size will lead to major shift in genome evolution. In particular, when effective population size is small, genetic drift becomes dominant over natural selection. The onset of self-fertilization is one evolutionary event considerably reducing effective size of populations. Theory predicts that this reduction should be more dramatic in organisms capable for haploid than for diploid selfing. Although theoretically well-grounded, this assertion received mixed experimental support. Here, we test this hypothesis by analyzing synonymous codon usage bias of genes in the model moss Physcomitrella patens frequently undergoing haploid selfing. In line with population genetic theory, we found that the effect of natural selection on synonymous codon usage bias is very weak. Our conclusion is supported by four independent lines of evidence: 1) Very weak or nonsignificant correlation between gene expression and codon usage bias, 2) no increased codon usage bias in more broadly expressed genes, 3) no evidence that codon usage bias would constrain synonymous and nonsynonymous divergence, and 4) predominant role of genetic drift on synonymous codon usage predicted by a model-based analysis. These findings show striking similarity to those observed in AT-rich genomes with weak selection for optimal codon usage and GC content overall. Our finding is in contrast to a previous study reporting adaptive codon usage bias in the moss P. patens. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  9. Controlling Selection Bias in Causal Inference

    Science.gov (United States)

    2012-02-01

    and cervix . Journal of the National Cancer Institute 11 1269–1275. Didelez, V., Kreiner, S. and Keiding, N. (2010). Graphical models for inference...Endometrial Cancer (Y ) was overestimated in the data studied. One of the symptoms of the use of Oe- strogen is vaginal bleeding (W ) (Fig. 1(c)), and the...whether similar bounds can be de - rived in the presence of selection bias. We will show that selection bias can be removed entirely through the use of

  10. Accounting for selection bias in species distribution models: An econometric approach on forested trees based on structural modeling

    Science.gov (United States)

    Ay, Jean-Sauveur; Guillemot, Joannès; Martin-StPaul, Nicolas K.; Doyen, Luc; Leadley, Paul

    2015-04-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global change on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of application on forested trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8 km). We also compared the output of the SSDM with outputs of a classical SDM in term of bioclimatic response curves and potential distribution under current climate. According to the species and the spatial resolution of the calibration dataset, shapes of bioclimatic response curves the modelled species distribution maps differed markedly between the SSDM and classical SDMs. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents a crucial step to account for economic constraints on tree

  11. Optogenetic stimulation in a computational model of the basal ganglia biases action selection and reward prediction error.

    Directory of Open Access Journals (Sweden)

    Pierre Berthet

    Full Text Available Optogenetic stimulation of specific types of medium spiny neurons (MSNs in the striatum has been shown to bias the selection of mice in a two choices task. This shift is dependent on the localisation and on the intensity of the stimulation but also on the recent reward history. We have implemented a way to simulate this increased activity produced by the optical flash in our computational model of the basal ganglia (BG. This abstract model features the direct and indirect pathways commonly described in biology, and a reward prediction pathway (RP. The framework is similar to Actor-Critic methods and to the ventral/dorsal distinction in the striatum. We thus investigated the impact on the selection caused by an added stimulation in each of the three pathways. We were able to reproduce in our model the bias in action selection observed in mice. Our results also showed that biasing the reward prediction is sufficient to create a modification in the action selection. However, we had to increase the percentage of trials with stimulation relative to that in experiments in order to impact the selection. We found that increasing only the reward prediction had a different effect if the stimulation in RP was action dependent (only for a specific action or not. We further looked at the evolution of the change in the weights depending on the stage of learning within a block. A bias in RP impacts the plasticity differently depending on that stage but also on the outcome. It remains to experimentally test how the dopaminergic neurons are affected by specific stimulations of neurons in the striatum and to relate data to predictions of our model.

  12. 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.

  13. Bias characterization of CrIS radiances at 399 selected channels with respect to NWP model simulations

    Science.gov (United States)

    Li, Xin; Zou, Xiaolei

    2017-11-01

    The Cross-track Infrared Sounder (CrIS) on board the Suomi National Polar-Orbiting Partnership (S-NPP) satellite is a hyperspectral Fourier Transform Spectrometer. In this study, biases of the 399 channels used in numerical weather prediction applications are characterized based on the differences between CrIS observations and model simulations in clear-sky conditions over ocean. The Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask is used for selecting CrIS clear-sky data. The global mean biases are within ± 1 K for most channels (more than 390). Biases for the channels not affected by trace gases other than water vapor in long-wave infrared (LWIR), middle-wave infrared (MWIR) and short-wave infrared (SWIR) bands vary from about - 0.5 to 0.3 K, - 0.2 to 0.8 K, and - 0.1 to 0.9 K, respectively. The scan-angle variations of biases are less than ± 0.4 K for all channels. The MWIR sounding channels have larger biases in middle and high latitudes than the tropics, which might be associated with latitudinal differences of water vapor variability. The SWIR CO2 channels are affected by nonlocal thermodynamic equilibrium (NLTE) in the upper stratosphere and the window channels could be affected by the sun glint effect, both of which are considered in model simulations. Biases of the NLTE affected channels during daytime are found to be 0.5-1 K higher than nighttime. The SWIR window channels have colder biases at Field of Regards (FORs) 6-15 than the other scan positions due to the sun glint effect.

  14. Selective Internal Radiation Therapy With Yttrium-90 Glass Microspheres: Biases and Uncertainties in Absorbed Dose Calculations Between Clinical Dosimetry Models.

    Science.gov (United States)

    Mikell, Justin K; Mahvash, Armeen; Siman, Wendy; Baladandayuthapani, Veera; Mourtada, Firas; Kappadath, S Cheenu

    2016-11-15

    To quantify differences that exist between dosimetry models used for 90 Y selective internal radiation therapy (SIRT). Retrospectively, 37 tumors were delineated on 19 post-therapy quantitative 90 Y single photon emission computed tomography/computed tomography scans. Using matched volumes of interest (VOIs), absorbed doses were reported using 3 dosimetry models: glass microsphere package insert standard model (SM), partition model (PM), and Monte Carlo (MC). Univariate linear regressions were performed to predict mean MC from SM and PM. Analysis was performed for 2 subsets: cases with a single tumor delineated (best case for PM), and cases with multiple tumors delineated (typical clinical scenario). Variability in PM from the ad hoc placement of a single spherical VOI to estimate the entire normal liver activity concentration for tumor (T) to nontumoral liver (NL) ratios (TNR) was investigated. We interpreted the slope of the resulting regression as bias and the 95% prediction interval (95%PI) as uncertainty. MC NL single represents MC absorbed doses to the NL for the single tumor patient subset; other combinations of calculations follow a similar naming convention. SM was unable to predict MC T single or MC T multiple (p>.12, 95%PI >±177 Gy). However, SM single was able to predict (p<.012) MC NL single , albeit with large uncertainties; SM single and SM multiple yielded biases of 0.62 and 0.71, and 95%PI of ±40 and ± 32 Gy, respectively. PM T single and PM T multiple predicted (p<2E-6) MC T single and MC T multiple with biases of 0.52 and 0.54, and 95%PI of ±38 and ± 111 Gy, respectively. The TNR variability in PM T single increased the 95%PI for predicting MC T single (bias = 0.46 and 95%PI = ±103 Gy). The TNR variability in PM T multiple modified the bias when predicting MC T multiple (bias = 0.32 and 95%PI = ±110 Gy). The SM is unable to predict mean MC tumor absorbed dose. The PM is statistically correlated with mean MC, but the

  15. Influence of monitoring data selection for optimization of a steady state multimedia model on the magnitude and nature of the model prediction bias.

    Science.gov (United States)

    Kim, Hee Seok; Lee, Dong Soo

    2017-11-01

    SimpleBox is an important multimedia model used to estimate the predicted environmental concentration for screening-level exposure assessment. The main objectives were (i) to quantitatively assess how the magnitude and nature of prediction bias of SimpleBox vary with the selection of observed concentration data set for optimization and (ii) to present the prediction performance of the optimized SimpleBox. The optimization was conducted using a total of 9604 observed multimedia data for 42 chemicals of four groups (i.e., polychlorinated dibenzo-p-dioxins/furans (PCDDs/Fs), polybrominated diphenyl ethers (PBDEs), phthalates, and polycyclic aromatic hydrocarbons (PAHs)). The model performance was assessed based on the magnitude and skewness of prediction bias. Monitoring data selection in terms of number of data and kind of chemicals plays a significant role in optimization of the model. The coverage of the physicochemical properties was found to be very important to reduce the prediction bias. This suggests that selection of observed data should be made such that the physicochemical property (such as vapor pressure, octanol-water partition coefficient, octanol-air partition coefficient, and Henry's law constant) range of the selected chemical groups be as wide as possible. With optimization, about 55%, 90%, and 98% of the total number of the observed concentration ratios were predicted within factors of three, 10, and 30, respectively, with negligible skewness. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Minimizing bias in biomass allometry: Model selection and log transformation of data

    Science.gov (United States)

    Joseph Mascaro; undefined undefined; Flint Hughes; Amanda Uowolo; Stefan A. Schnitzer

    2011-01-01

    Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the raditional approach of log-transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models....

  17. Two-Year versus One-Year Head Start Program Impact: Addressing Selection Bias by Comparing Regression Modeling with Propensity Score Analysis

    Science.gov (United States)

    Leow, Christine; Wen, Xiaoli; Korfmacher, Jon

    2015-01-01

    This article compares regression modeling and propensity score analysis as different types of statistical techniques used in addressing selection bias when estimating the impact of two-year versus one-year Head Start on children's school readiness. The analyses were based on the national Head Start secondary dataset. After controlling for…

  18. 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.

  19. Confirmation biases in selective exposure to political online information : Source bias vs. content bias

    NARCIS (Netherlands)

    Westerwick, Axel; Johnson, Benjamin K.; Knobloch-Westerwick, Silvia

    2017-01-01

    The present work examines the role of source vs. content cues for the confirmation bias, in which recipients spend more time with content aligning with preexisting attitudes. In addition to testing how both source and content cues facilitate this biased pattern of selective exposure, the study

  20. How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models.

    Science.gov (United States)

    Nuthmann, Antje; Einhäuser, Wolfgang; Schütz, Immo

    2017-01-01

    Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead ("central bias"). This problem is further exacerbated in the context of model comparisons, because some-but not all-models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox "GridFix" available.

  1. Heterogeneous Causal Effects and Sample Selection Bias

    DEFF Research Database (Denmark)

    Breen, Richard; Choi, Seongsoo; Holm, Anders

    2015-01-01

    The role of education in the process of socioeconomic attainment is a topic of long standing interest to sociologists and economists. Recently there has been growing interest not only in estimating the average causal effect of education on outcomes such as earnings, but also in estimating how...... causal effects might vary over individuals or groups. In this paper we point out one of the under-appreciated hazards of seeking to estimate heterogeneous causal effects: conventional selection bias (that is, selection on baseline differences) can easily be mistaken for heterogeneity of causal effects....... This might lead us to find heterogeneous effects when the true effect is homogenous, or to wrongly estimate not only the magnitude but also the sign of heterogeneous effects. We apply a test for the robustness of heterogeneous causal effects in the face of varying degrees and patterns of selection bias...

  2. How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models

    Directory of Open Access Journals (Sweden)

    Antje Nuthmann

    2017-10-01

    Full Text Available Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”. This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM, building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.

  3. 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.

  4. Subjective bias in PRA - the role of judgement in the selection of plant modeling input data for establishing safety goals

    International Nuclear Information System (INIS)

    Haenni, H.P.; Smith, A.L.

    1986-01-01

    The sources of the uncertainties are generally accepted as modeling deficiencies, lack of completeness in the analysis and the input data deficiencies. The role of judgement in selecting input data for establishing safety goals will be discussed. As an example, a safety goal for unacceptable radioactivity release will be considered. Two analysts are discussing the introduction of an emergency service water system, applying a different way of engineering judgement. Using PRA combined with safety goals as a decision-making tool it could have an important influence on the design and the costs of the plant. The suitability of the methodology has to be generally accepted before it will be established as a regulatory requirement. (orig.)

  5. 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.

  6. Selection bias at the heterosexual HIV-1 transmission bottleneck

    Science.gov (United States)

    Carlson, Jonathan M.; Schaefer, Malinda; Monaco, Daniela C.; Batorsky, Rebecca; Claiborne, Daniel T.; Prince, Jessica; Deymier, Martin J.; Ende, Zachary S.; Klatt, Nichole R.; DeZiel, Charles E.; Lin, Tien-Ho; Peng, Jian; Seese, Aaron M.; Shapiro, Roger; Frater, John; Ndung’u, Thumbi; Tang, Jianming; Goepfert, Paul; Gilmour, Jill; Price, Matt A.; Kilembe, William; Heckerman, David; Goulder, Philip J.R.; Allen, Todd M.; Allen, Susan; Hunter, Eric

    2014-01-01

    SUMMARY Introduction Heterosexual HIV-1 transmission is an inefficient process with rates reported at heterosexual transmission pairs, for whom plasma samples were available for both the donor and recipient partner soon after transmission, and compared the viral sequences obtained from each partner to identify features that predicted whether the majority amino acid observed at any particular position in the donor was transmitted. We focused attention on two features: viral genetic characteristics that correlate with viral fitness, and clinical factors that influence transmission. Statistical modeling indicates that the former will be favored for transmission, while the latter will nullify this relative advantage. Results We observed a highly significant selection bias that favors the transmission of amino acids associated with increased fitness. These features included the frequency of the amino acid in the study cohort, the relative advantage of the amino acid with respect to the stability of the protein, and features related to immune escape and compensation. This selection bias was reduced in couples with high risk of transmission. In particular, significantly less selection bias was observed in women and in men with genital inflammation, compared to healthy men, suggesting a more permissive environment in the female than male genital tract. Consistent with this observation, viruses transmitted to women were characterized by lower predicted fitness than those in men. The presence of amino acids favored during transmission predicted which individual virus within a donor was transmitted to their partner, while chronically infected individuals with viral populations characterized by a predominance of these amino acids were more likely to transmit to their partners. Conclusion These data highlight the clear selection biases that benefit fitter viruses during transmission in the context of a stochastic process. That such biases exist, and are tempered by certain risk

  7. Impact of Selection Bias on Estimation of Subsequent Event Risk

    NARCIS (Netherlands)

    Hu, Yi Juan; Schmidt, Amand F.; Dudbridge, Frank; Holmes, Michael V; Brophy, James M.; Tragante, Vinicius; Li, Ziyi; Liao, Peizhou; Quyyumi, Arshed A.; McCubrey, Raymond O.; Horne, Benjamin D.; Hingorani, Aroon D; Asselbergs, Folkert W; Patel, Riyaz S.; Long, Qi; Åkerblom, Axel; Algra, Ale; Allayee, Hooman; Almgren, Peter; Anderson, Jeffrey L.; Andreassi, Maria G.; Anselmi, Chiara V.; Ardissino, Diego; Arsenault, Benoit J.; Ballantyne, Christie M.; Baranova, Ekaterina V.; Behloui, Hassan; Bergmeijer, Thomas O; Bezzina, Connie R; Bjornsson, Eythor; Body, Simon C.; Boeckx, Bram; Boersma, Eric H.; Boerwinkle, Eric; Bogaty, Peter; Braund, Peter S; Breitling, Lutz P.; Brenner, Hermann; Briguori, Carlo; Brugts, Jasper J.; Burkhardt, Ralph; Cameron, Vicky A.; Carlquist, John F.; Carpeggiani, Clara; Carruthers, Kathryn F.; Casu, Gavino; Condorelli, Gianluigi; Cresci, Sharon; Danchin, Nicolas; de Faire, Ulf; Deanfield, John; Delgado, Graciela; Deloukas, Panos; Direk, Kenan; Doughty, Robert N.; Drexel, Heinz; Duarte, Nubia E.; Dubé, Marie Pierre; Dufresne, Line; Engert, James C; Eriksson, Niclas; Fitzpatrick, Natalie; Foco, Luisa; Ford, Ian; Fox, Keith A; Gigante, Bruna; Gijsberts, Crystel M.; Girelli, Domenico; Gong, Yan; Gudbjartsson, Daniel F.; Hagström, Emil; Hartiala, Jaana; Hazen, Stanley L.; Held, Claes; Helgadottir, Anna; Hemingway, Harry; Heydarpour, Mahyar; Hoefer, Imo E.; Hovingh, G. Kees; Hubacek, Jaroslav A; James, Stefan; Johnson, Julie A; Jukema, J Wouter; Kaczor, Marcin P.; Kaminski, Karol A.; Kettner, Jiri; Kiliszek, Marek; Kleber, Marcus; Klungel, Olaf H.; Kofink, Daniel; Kohonen, Mika; Kotti, Salma; Kuukasjärvi, Pekka; Lagerqvist, Bo; Lambrechts, Diether; Lang, Chim C; Laurikka, Jari O.; Leander, Karin; Lee, Vei Vei; Lehtimäki, Terho; Leiherer, Andreas; Lenzini, Petra A.; Levin, Daniel; Lindholm, Daniel; Lokki, Marja-Liisa; Lotufo, Paulo A; Lyytikäinen, Leo-Pekka; Mahmoodi, B. Khan; Maitland-Van Der Zee, Anke H.; Martinelli, Nicola; März, Winfried; Marziliano, Nicola; McPherson, Ruth; Melander, Olle; Mons, Ute; Muehlschlegel, Jochen D.; Muhlestein, Joseph B.; Nelson, Cristopher P.; Cheh, Chris Newton; Olivieri, Oliviero; Opolski, Grzegorz; Palmer, Colin Na; Pare, Guillaume; Pasterkamp, Gerard; Pepine, Carl J; Pepinski, Witold; Pereira, Alexandre C.; Pilbrow, Anna P.; Pilote, Louise; Pitha, Jan; Ploski, Rafal; Richards, A. Mark; Saely, Christoph H.; Samani, Nilesh J; Samman-Tahhan, Ayman; Sanak, Marek; Sandesara, Pratik B.; Sattar, Naveed; Scholz, Markus; Siegbahn, Agneta; Simon, Tabassome; Sinisalo, Juha; Smith, J. Gustav; Spertus, John A.; Stefansson, Kari; Stewart, Alexandre F R; Stott, David J.; Szczeklik, Wojciech; Szpakowicz, Anna; Tanck, Michael W.T.; Tang, Wilson H.; Tardif, Jean-Claude; Ten Berg, Jur M.; Teren, Andrej; Thanassoulis, George; Thiery, Joachim; Thorgeirsson, Gudmundur; Thorleifsson, Gudmar; Thorsteinsdottir, Unnur; Timmis, Adam; Trompet, Stella; Van de Werf, Frans; van der Graaf, Yolanda; Van Der Haarst, Pim; van der Laan, Sander W; Vilmundarson, Ragnar O.; Virani, Salim S.; Visseren, Frank L J; Vlachopoulou, Efthymia; Wallentin, Lars; Waltenberger, Johannes; Wauters, Els; Wilde, Arthur A M

    2017-01-01

    Background - Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic

  8. Reviewer selection biases editorial decisions on manuscripts.

    Science.gov (United States)

    Hausmann, Laura; Schweitzer, Barbara; Middleton, Frank A; Schulz, Jörg B

    2018-01-27

    Many journals, including the Journal of Neurochemistry, enable authors to list peer reviewers as "preferred" or "opposed" suggestions to the editor. At the Journal of Neurochemistry, the handling editor (HE) may follow recommendations or select non-author-suggested reviewers (non-ASRs). We investigated whether selection of author-suggested reviewers (ASRs) influenced decisions on a paper, and whether differences might be related to a reviewers', editor's or manuscript's geographical location. In this retrospective analysis, we compared original research articles submitted to the Journal of Neurochemistry from 2013 through 2016, that were either reviewed exclusively by non-ASRs, by at least one ASR, by at least one reviewer marked by the author as "opposed", or none. Manuscript outcome, reviewer rating of manuscript quality, rating of the reviewers' performance by the editor (R-score), time to review, and the country of the editor, reviewers and manuscript author were analyzed using non-parametric rank-based comparisons, chi-square (x 2 ) analysis, multivariate linear regression, one-way analysis of variance, and inter-rater reliability determination. Original research articles that had been reviewed by at least one ASR stood a higher chance of being accepted (525/1006 = 52%) than papers that had been reviewed by non-ASRs only (579/1800 = 32%). An article was 2.4 times more likely to be accepted than rejected by an ASR compared to a non-ASR (Pearson's x 2 (1) = 181.3, previewers' recommendation but had a balancing role: Rates of recommendation from reviewers for rejection were 11.2% (139/1241) with ASRs vs. 29.0% (1379/4755) with non-ASRs (this is a ratio of 0.39 where 1 means no difference between rejection rates for both groups), whereas the proportion of final decisions to reject was 24.7% (248/1006) vs. 45.7% (822/1800) (a ratio of 0.54, considerably closer to 1). Recommendations by non-ASRs were more favorable for manuscripts from USA/Canada and Europe than for

  9. Missing Data Bias on a Selective Hedging Strategy

    Directory of Open Access Journals (Sweden)

    Kiss Gábor Dávid

    2017-03-01

    Full Text Available Foreign exchange rates affect corporate profitability both on the macro and cash-flow level. The current study analyses the bias of missing data on a selective hedging strategy, where currency options are applied in case of Value at Risk (1% signs. However, there can be special occasions when one or some data is missing due to lack of a trading activity. This paper focuses on the impact of different missing data handling methods on GARCH and Value at Risk model parameters, because of selective hedging and option pricing based on them. The main added value of the current paper is the comparison of the impact of different methods, such as listwise deletion, mean substitution, and maximum likelihood based Expectation Maximization, on risk management because this subject has insufficient literature. The current study tested daily closing data of floating currencies from Kenya (KES, Ghana (GHS, South Africa (ZAR, Tanzania (TZS, Uganda (UGX, Gambia (GMD, Madagascar (MGA and Mozambique (MZN in USD denomination against EUR/USD rate between March 8, 2000 and March 6, 2015 acquired from the Bloomberg database. Our results suggested the biases of missingness on Value at Risk and volatility models, presenting significant differences among the number of extreme fluctuations or model parameters. A selective hedging strategy can have different expenditures due to the choice of method. This paper suggests the usage of mean substitution or listwise deletion for daily financial time series due to their tendency to have a close to zero first momentum

  10. The impact of selection bias in randomized multi-arm parallel group clinical trials.

    Directory of Open Access Journals (Sweden)

    Diane Uschner

    Full Text Available The impact of selection bias on the results of clinical trials has been analyzed extensively for trials of two treatments, yet its impact in multi-arm trials is still unknown. In this paper, we investigate selection bias in multi-arm trials by its impact on the type I error probability. We propose two models for selection bias, so-called biasing policies, that both extend the classic guessing strategy by Blackwell and Hodges. We derive the distribution of the F-test statistic under the misspecified outcome model and provide a formula for the type I error probability under selection bias. We apply the presented approach to quantify the influence of selection bias in multi-arm trials with increasing number of treatment groups using a permuted block design for different assumptions and different biasing strategies. Our results confirm previous findings that smaller block sizes lead to a higher proportion of sequences with inflated type I error probability. Astonishingly, our results also show that the proportion of sequences with inflated type I error probability remains constant when the number of treatment groups is increased. Realizing that the impact of selection bias cannot be completely eliminated, we propose a bias adjusted statistical model and show that the power of the statistical test is only slightly deflated for larger block sizes.

  11. Meta-Regression Approximations to Reduce Publication Selection Bias

    Science.gov (United States)

    Stanley, T. D.; Doucouliagos, Hristos

    2014-01-01

    Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with…

  12. 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...

  13. Target selection biases from recent experience transfer across effectors.

    Science.gov (United States)

    Moher, Jeff; Song, Joo-Hyun

    2016-02-01

    Target selection is often biased by an observer's recent experiences. However, not much is known about whether these selection biases influence behavior across different effectors. For example, does looking at a red object make it easier to subsequently reach towards another red object? In the current study, we asked observers to find the uniquely colored target object on each trial. Randomly intermixed pre-trial cues indicated the mode of action: either an eye movement or a visually guided reach movement to the target. In Experiment 1, we found that priming of popout, reflected in faster responses following repetition of the target color on consecutive trials, occurred regardless of whether the effector was repeated from the previous trial or not. In Experiment 2, we examined whether an inhibitory selection bias away from a feature could transfer across effectors. While priming of popout reflects both enhancement of the repeated target features and suppression of the repeated distractor features, the distractor previewing effect isolates a purely inhibitory component of target selection in which a previewed color is presented in a homogenous display and subsequently inhibited. Much like priming of popout, intertrial suppression biases in the distractor previewing effect transferred across effectors. Together, these results suggest that biases for target selection driven by recent trial history transfer across effectors. This indicates that representations in memory that bias attention towards or away from specific features are largely independent from their associated actions.

  14. The Selective Advantage of Synonymous Codon Usage Bias in Salmonella.

    Directory of Open Access Journals (Sweden)

    Gerrit Brandis

    2016-03-01

    Full Text Available The genetic code in mRNA is redundant, with 61 sense codons translated into 20 different amino acids. Individual amino acids are encoded by up to six different codons but within codon families some are used more frequently than others. This phenomenon is referred to as synonymous codon usage bias. The genomes of free-living unicellular organisms such as bacteria have an extreme codon usage bias and the degree of bias differs between genes within the same genome. The strong positive correlation between codon usage bias and gene expression levels in many microorganisms is attributed to selection for translational efficiency. However, this putative selective advantage has never been measured in bacteria and theoretical estimates vary widely. By systematically exchanging optimal codons for synonymous codons in the tuf genes we quantified the selective advantage of biased codon usage in highly expressed genes to be in the range 0.2-4.2 x 10-4 per codon per generation. These data quantify for the first time the potential for selection on synonymous codon choice to drive genome-wide sequence evolution in bacteria, and in particular to optimize the sequences of highly expressed genes. This quantification may have predictive applications in the design of synthetic genes and for heterologous gene expression in biotechnology.

  15. 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.

  16. Modelling biased human trust dynamics

    NARCIS (Netherlands)

    Hoogendoorn, M.; Jaffry, S.W.; Maanen, P.P. van; Treur, J.

    2013-01-01

    Abstract. Within human trust related behaviour, according to the literature from the domains of Psychology and Social Sciences often non-rational behaviour can be observed. Current trust models that have been developed typically do not incorporate non-rational elements in the trust formation

  17. Codon usage bias in animals: disentangling the effects of natural selection, effective population size and GC-biased gene conversion.

    Science.gov (United States)

    Galtier, N; Roux, C; Rousselle, M; Romiguier, J; Figuet, E; Glémin, S; Bierne, N; Duret, L

    2018-01-30

    Selection on codon usage bias is well documented in a number of microorganisms. Whether codon usage is also generally shaped by natural selection in large organisms, despite their relatively small effective population size (Ne), is unclear. In animals, the population genetics of codon usage bias has only been studied in a handful of model organisms so far, and can be affected by confounding, non-adaptive processes such as GC-biased gene conversion and experimental artefacts. Using population transcriptomics data we analysed the relationship between codon usage, gene expression, allele frequency distribution and recombination rate in 30 non-model species of animals, each from a different family, covering a wide range of effective population sizes. We disentangled the effects of translational selection and GC-biased gene conversion on codon usage by separately analysing GC-conservative and GC-changing mutations. We report evidence for effective translational selection on codon usage in large-Ne species of animals, but not in small-Ne ones, in agreement with the nearly neutral theory of molecular evolution. C- and T-ending codons tend to be preferred over synonymous G- and A-ending ones, for reasons that remain to be determined. In contrast, we uncovered a conspicuous effect of GC-biased gene conversion, which is widespread in animals and the main force determining the fate of AT↔GC mutations. Intriguingly, the strength of its effect was uncorrelated with Ne. © The Author 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  18. Sex-Specific Selection and Sex-Biased Gene Expression in Humans and Flies.

    Directory of Open Access Journals (Sweden)

    Changde Cheng

    2016-09-01

    Full Text Available Sexual dimorphism results from sex-biased gene expression, which evolves when selection acts differently on males and females. While there is an intimate connection between sex-biased gene expression and sex-specific selection, few empirical studies have studied this relationship directly. Here we compare the two on a genome-wide scale in humans and flies. We find a distinctive "Twin Peaks" pattern in humans that relates the strength of sex-specific selection, quantified by genetic divergence between male and female adults at autosomal loci, to the degree of sex-biased expression. Genes with intermediate degrees of sex-biased expression show evidence of ongoing sex-specific selection, while genes with either little or completely sex-biased expression do not. This pattern apparently results from differential viability selection in males and females acting in the current generation. The Twin Peaks pattern is also found in Drosophila using a different measure of sex-specific selection acting on fertility. We develop a simple model that successfully recapitulates the Twin Peaks. Our results suggest that many genes with intermediate sex-biased expression experience ongoing sex-specific selection in humans and flies.

  19. Modeling Rater Disagreement for ADHD: Are Parents or Teachers Biased?

    Science.gov (United States)

    Hartman, Christie A.; Rhee, Soo H.; Willcutt, Erik G.; Pennington, Bruce F

    2007-01-01

    The present study is the first to utilize twin modeling to examine whether parent-teacher disagreement for ADHD ratings is due to parent or teacher bias, or due to raters observing different but valid ADHD behaviors. A joint analysis was conducted with 106 twin pairs, including twins selected for ADHD and control twin pairs. Total ADHD scores were…

  20. Self-selection bias in estimated wage premiums for earnings risk

    NARCIS (Netherlands)

    Jacobs, B.; Hartog, J.; Vijverberg, W.

    2009-01-01

    This note develops a simple occupational choice model to examine three types of selection biases that may occur in empirically estimating the premium for uncertain wages. Individuals may select themselves into risky (wage-uncertain) jobs because they have (1) lower risk aversion, or (2) lower income

  1. Retransformation bias in a stem profile model

    Science.gov (United States)

    Raymond L. Czaplewski; David Bruce

    1990-01-01

    An unbiased profile model, fit to diameter divided by diameter at breast height, overestimated volume of 5.3-m log sections by 0.5 to 3.5%. Another unbiased profile model, fit to squared diameter divided by squared diameter at breast height, underestimated bole diameters by 0.2 to 2.1%. These biases are caused by retransformation of the predicted dependent variable;...

  2. 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.

  3. 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…

  4. A cardinal orientation bias in scene-selective visual cortex.

    Science.gov (United States)

    Nasr, Shahin; Tootell, Roger B H

    2012-10-24

    It has long been known that human vision is more sensitive to contours at cardinal (horizontal and vertical) orientations, compared with oblique orientations; this is the "oblique effect." However, the real-world relevance of the oblique effect is not well understood. Experiments here suggest that this effect is linked to scene perception, via a common bias in the image statistics of scenes. This statistical bias for cardinal orientations is found in many "carpentered environments" such as buildings and indoor scenes, and some natural scenes. In Experiment 1, we confirmed the presence of a perceptual oblique effect in a specific set of scene stimuli. Using those scenes, we found that a well known "scene-selective" visual cortical area (the parahippocampal place area; PPA) showed distinctively higher functional magnetic resonance imaging (fMRI) activity to cardinal versus oblique orientations. This fMRI-based oblique effect was not observed in other cortical areas (including scene-selective areas transverse occipital sulcus and retrosplenial cortex), although all three scene-selective areas showed the expected inversion effect to scenes. Experiments 2 and 3 tested for an analogous selectivity for cardinal orientations using computer-generated arrays of simple squares and line segments, respectively. The results confirmed the preference for cardinal orientations in PPA, thus demonstrating that the oblique effect can also be produced in PPA by simple geometrical images, with statistics similar to those in scenes. Thus, PPA shows distinctive fMRI selectivity for cardinal orientations across a broad range of stimuli, which may reflect a perceptual oblique effect.

  5. Validation of models with proportional bias

    Directory of Open Access Journals (Sweden)

    Salvador Medina-Peralta

    2017-01-01

    Full Text Available Objective. This paper presents extensions to Freese’s statistical method for model-validation when proportional bias (PB is present in the predictions. The method is illustrated with data from a model that simulates grassland growth. Materials and methods. The extensions to validate models with PB were: the maximum anticipated error for the original proposal, hypothesis testing, and the maximum anticipated error for the alternative proposal, and the confidence interval for a quantile of error distribution. Results. The tested model had PB, which once removed, and with a confidence level of 95%, the magnitude of error does not surpass 1225.564 kg ha-1. Therefore, the validated model can be used to predict grassland growth. However, it would require a fit of its structure based on the presence of PB. Conclusions. The extensions presented to validate models with PB are applied without modification in the model structure. Once PB is corrected, the confidence interval for the quantile 1-α of the error distribution enables a higher bound for the magnitude of the prediction error and it can be used to evaluate the evolution of the model for a system prediction.

  6. A Heckman Selection- t Model

    KAUST Repository

    Marchenko, Yulia V.

    2012-03-01

    Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test 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 Association.

  7. Self-selection bias and manufacturing firms' demand for water recirculation

    OpenAIRE

    Steven Renzetti; Joel Brueau; Michel Villeneuve

    2009-01-01

    Relatively little is known of the factors which might influence firms' water recirculation decisions. This paper estimates an econometric model that accounts for potential self-selection bias and considers two facets of firms' recirculation behaviour: first, the discrete decision of whether to recirculate and, second, the decision of how much to recirculate. The model is estimated by applying the Heckman two-stage estimation procedure to data from Environment Canada's Industrial Water Use Sur...

  8. 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)

  9. Selection bias due to parity-conditioning in studies of time trends in fertility

    DEFF Research Database (Denmark)

    Sallmén, Markku; Bonde, Jens Peter; Lindbohm, Marja-Liisa

    2015-01-01

    BACKGROUND: Studies of couple fertility over time have often examined study populations with broad age ranges at a cross-section of time. An increase in fertility has been observed in studies that followed episodes of fertility events either prospectively among nulliparous women or retrospectively....... METHODS: A study with prospective follow-up was taken as the model to assess the validity of fertility studies. We demonstrate the potential for selection bias using causal graphs and nationwide birth statistics and other demographic data. We tested the existence of parity-conditioning bias in data......-conditioning varies by sampling frame. A prospective multidecade study with representative sampling of birth cohorts and follow-up from menarche to menopause would bypass the described bias....

  10. Duration on unemployment: geographic mobility and selectivity bias.

    Science.gov (United States)

    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

  11. Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

    Directory of Open Access Journals (Sweden)

    D. Del Giudice

    2013-10-01

    Full Text Available Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall or/and output (runoff of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on

  12. Hummingbird responses to gender-biased nectar production: are nectar biases maintained by natural or sexual selection?

    Science.gov (United States)

    Carlson, Jane E

    2008-08-07

    Pollinators mediate the evolution of secondary floral traits through both natural and sexual selection. Gender-biased nectar, for example, could be maintained by one or both, depending on the interactions between plants and pollinators. Here, I investigate pollinator responses to gender-biased nectar using the dichogamous herb Chrysothemis friedrichsthaliana (Gesneriaceae) which produces more nectar during the male floral phase. Previous research showed that the hummingbird pollinator Phaethornis striigularis visited male-phase flowers more often than female-phase flowers, and multiple visits benefited male more than female fecundity. If sexual selection maintains male-biased rewards, hummingbirds should prefer more-rewarding flowers independent of floral gender. If, however, differential rewards are partially maintained through natural selection, hummingbirds should respond to asymmetry with visits that reduce geitonogamy, i.e. selfing and pollen discounting. In plants with male biases, these visit types include single-flower visits and movements from low to high rewards. To test these predictions, I manipulated nectar asymmetry between pairs of real or artificial flowers on plants and recorded foraging behaviour. I also assessed maternal costs of selfing using hand pollinations. For plants with real flowers, hummingbirds preferred more-rewarding flowers and male-phase morphology, the latter possibly owing to previous experience. At artificial arrays, hummingbirds responded to extreme reward asymmetry with increased single-flower visits; however, they moved from high to low rewards more often than low to high. Finally, selfed flowers did not produce inferior seeds. In summary, sexual selection, more so than geitonogamy avoidance, maintains nectar biases in C. friedrichsthaliana, in one of the clearest examples of sexual selection in plants, to date.

  13. 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.

  14. 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.

  15. Conceptual and methodological biases in network models.

    Science.gov (United States)

    Lamm, Ehud

    2009-10-01

    Many natural and biological phenomena can be depicted as networks. Theoretical and empirical analyses of networks have become prevalent. I discuss theoretical biases involved in the delineation of biological networks. The network perspective is shown to dissolve the distinction between regulatory architecture and regulatory state, consistent with the theoretical impossibility of distinguishing a priori between "program" and "data." The evolutionary significance of the dynamics of trans-generational and interorganism regulatory networks is explored and implications are presented for understanding the evolution of the biological categories development-heredity, plasticity-evolvability, and epigenetic-genetic.

  16. Unconscious bias against introverts in the recruitment and selection process

    OpenAIRE

    Braathen, Vilde Marie Lie; Sørensen, Martine Waller

    2017-01-01

    Masteroppgave(MSc) in Master of Science in Leadership and Organizational Psychology - Handelshøyskolen BI, 2017 The current study is an attempt to expand upon the limited research area of unconscious bias against certain personality types that may emerge during the employment interview. We proposed that an extraverted personality type would be favoured in the employment interview, and this was based on the notion that the interview situation, which is inherently a social intera...

  17. 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.

  18. 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.

  19. Linking statistical bias description to multiobjective model calibration

    Science.gov (United States)

    Reichert, P.; Schuwirth, N.

    2012-09-01

    In the absence of model deficiencies, simulation results at the correct parameter values lead to an unbiased description of observed data with remaining deviations due to observation errors only. However, this ideal cannot be reached in the practice of environmental modeling, because the required simplified representation of the complex reality by the model and errors in model input lead to errors that are reflected in biased model output. This leads to two related problems: First, ignoring bias of output in the statistical model description leads to bias in parameter estimates, model predictions and, in particular, in the quantification of their uncertainty. Second, as there is no objective choice of how much bias to accept in which output variable, it is not possible to design an "objective" model calibration procedure. The first of these problems has been addressed by introducing a statistical (Bayesian) description of bias, the second by suggesting the use of multiobjective calibration techniques that cannot easily be used for uncertainty analysis. We merge the ideas of these two approaches by using the prior of the statistical bias description to quantify the importance of multiple calibration objectives. This leads to probabilistic inference and prediction while still taking multiple calibration objectives into account. The ideas and technical details of the suggested approach are outlined and a didactical example as well as an application to environmental data are provided to demonstrate its practical feasibility and computational efficiency.

  20. Adjustment for selection bias in observational studies with application to the analysis of autopsy data.

    Science.gov (United States)

    Haneuse, S; Schildcrout, J; Crane, P; Sonnen, J; Breitner, J; Larson, E

    2009-01-01

    The interpretation of neuropathological studies of dementia and Alzheimer's disease is complicated by potential selection mechanisms that can drive whether or not a study participant is observed to undergo autopsy. Notwithstanding this, there appears to have been little emphasis placed on potential selection bias in published reports from population-based neuropathological studies of dementia. We provide an overview of methodological issues relating to the identification of and adjustment for selection bias. When information is available on factors that govern selection, inverse-probability weighting provides an analytic approach to adjust for selection bias. The weights help alleviate bias by serving to bridge differences between the population from which the observed data may be viewed as a representative sample and the target population, identified as being of scientific interest. We illustrate the methods with data obtained from the Adult Changes in Thought study. Adjustment for potential selection bias yields substantially strengthened association between neuropathological measurements and risk of dementia. Armed with analytic techniques to adjust for selection bias and to ensure generalizability of results from population-based neuropathological studies, researchers should consider incorporating information related to selection into their data collection schemes. 2009 S. Karger AG, Basel.

  1. 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.

  2. 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.

  3. Empirical aspects about Heckman Procedure Application: Is there sample selection bias in the Brazilian Industry

    OpenAIRE

    Flávio Kaue Fiuza-Moura; Katy Maia

    2015-01-01

    There are several labor market researches whose main goal is to analyze the probability of employment and the structure of wage determination and, for empirical purposes, most of these researches deploy Heckman sample selection bias hazard detection and correction procedure. However, few Brazilian studies are focused in this procedure applicability, especially concerning specific industries. This paper aims to approach these issues by testing the existence of sample selection bias in Brazilia...

  4. 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.

  5. North Atlantic climate model bias influence on multiyear predictability

    Science.gov (United States)

    Wu, Y.; Park, T.; Park, W.; Latif, M.

    2018-01-01

    The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

  6. 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.

  7. Selective Attention and Drug Related Attention Bias in Methadone Maintenance Patients

    Directory of Open Access Journals (Sweden)

    Majid Nejati

    2011-12-01

    Full Text Available One of the main problems of the drug abusers is drug related attention bias, which causes craving, and as a result drive the drug abusers to take narcotics. Methadone is used as a maintenance treatment for drug abusers. The purpose of this study is evaluation of the effect of Methadone maintenance therapy (MMT on selective attention and drug related attention bias. This study investigated drug cue-related attention bias and selective attention in 16 methadone-maintained patients before and 45 days after methadone therapy period. Stroop color-word test and addiction Stroop test were used as measurement methods. Results show less reaction time and higher accuracy in Color-Word Stroop Test after MMT and less delay for addict related word in addiction Stroop test. It is concluded that methadone can improve selective attention capability and reduce drug related attention bias.

  8. Applying quantitative bias analysis to estimate the plausible effects of selection bias in a cluster randomised controlled trial: secondary analysis of the Primary care Osteoarthritis Screening Trial (POST).

    Science.gov (United States)

    Barnett, L A; Lewis, M; Mallen, C D; Peat, G

    2017-12-04

    Selection bias is a concern when designing cluster randomised controlled trials (c-RCT). Despite addressing potential issues at the design stage, bias cannot always be eradicated from a trial design. The application of bias analysis presents an important step forward in evaluating whether trial findings are credible. The aim of this paper is to give an example of the technique to quantify potential selection bias in c-RCTs. This analysis uses data from the Primary care Osteoarthritis Screening Trial (POST). The primary aim of this trial was to test whether screening for anxiety and depression, and providing appropriate care for patients consulting their GP with osteoarthritis would improve clinical outcomes. Quantitative bias analysis is a seldom-used technique that can quantify types of bias present in studies. Due to lack of information on the selection probability, probabilistic bias analysis with a range of triangular distributions was also used, applied at all three follow-up time points; 3, 6, and 12 months post consultation. A simple bias analysis was also applied to the study. Worse pain outcomes were observed among intervention participants than control participants (crude odds ratio at 3, 6, and 12 months: 1.30 (95% CI 1.01, 1.67), 1.39 (1.07, 1.80), and 1.17 (95% CI 0.90, 1.53), respectively). Probabilistic bias analysis suggested that the observed effect became statistically non-significant if the selection probability ratio was between 1.2 and 1.4. Selection probability ratios of > 1.8 were needed to mask a statistically significant benefit of the intervention. The use of probabilistic bias analysis in this c-RCT suggested that worse outcomes observed in the intervention arm could plausibly be attributed to selection bias. A very large degree of selection of bias was needed to mask a beneficial effect of intervention making this interpretation less plausible.

  9. Diurnal cloud cycle biases in climate models.

    Science.gov (United States)

    Yin, Jun; Porporato, Amilcare

    2017-12-22

    Clouds' efficiency at reflecting solar radiation and trapping the terrestrial radiation is strongly modulated by the diurnal cycle of clouds (DCC). Much attention has been paid to mean cloud properties due to their critical role in climate projections; however, less research has been devoted to the DCC. Here we quantify the mean, amplitude, and phase of the DCC in climate models and compare them with satellite observations and reanalysis data. While the mean appears to be reliable, the amplitude and phase of the DCC show marked inconsistencies, inducing overestimation of radiation in most climate models. In some models, DCC appears slightly shifted over the ocean, likely as a result of tuning and fortuitously compensating the large DCC errors over the land. While this model tuning does not seem to invalidate climate projections because of the limited DCC response to global warming, it may potentially increase the uncertainty of climate predictions.

  10. 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.

  11. Intron evolution in Neurospora: the role of mutational bias and selection.

    Science.gov (United States)

    Sun, Yu; Whittle, Carrie A; Corcoran, Pádraic; Johannesson, Hanna

    2015-01-01

    We used comparative and population genomics to study intron evolutionary dynamics in the fungal model genus Neurospora. For our investigation, we used well-annotated genomes of N. crassa, N. discreta, and N. tetrasperma, and 92 resequenced genomes of N. tetrasperma from natural populations. By analyzing the four well-annotated genomes, we identified 9495 intron sites in 7619 orthologous genes. Our data supports nonhomologous end joining (NHEJ) and tandem duplication as mechanisms for intron gains in the genus and the RT-mRNA process as a mechanism for intron loss. We found a moderate intron gain rate (5.78-6.89 × 10(-13) intron gains per nucleotide site per year) and a high intron loss rate (7.53-13.76 × 10(-10) intron losses per intron sites per year) as compared to other eukaryotes. The derived intron gains and losses are skewed to high frequencies, relative to neutral SNPs, in natural populations of N. tetrasperma, suggesting that selection is involved in maintaining a high intron turnover. Furthermore, our analyses of the association between intron population-level frequency and genomic features suggest that selection is involved in shaping a 5' intron position bias and a low intron GC content. However, intron sequence analyses suggest that the gained introns were not exposed to recent selective sweeps. Taken together, this work contributes to our understanding of the importance of mutational bias and selection in shaping the intron distribution in eukaryotic genomes. © 2015 Sun et al.; Published by Cold Spring Harbor Laboratory Press.

  12. Bias in the Correlated Uniqueness Model for MTMM Data

    Science.gov (United States)

    Conway, James M.; Lievens, Filip; Scullen, Steven E.; Lance, Charles E.

    2004-01-01

    This simulation investigates bias in trait factor loadings and intercorrelations when analyzing multitrait-multimethod (MTMM) data using the correlated uniqueness (CU) confirmatory factor analysis (CFA) model. A theoretical weakness of the CU model is the assumption of uncorrelated methods. However, previous simulation studies have shown little…

  13. 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.

  14. 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.

  15. 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.

  16. Quantifying Selection Bias in National Institute of Health Stroke Scale Data Documented in an Acute Stroke Registry.

    Science.gov (United States)

    Thompson, Michael P; Luo, Zhehui; Gardiner, Joseph; Burke, James F; Nickles, Adrienne; Reeves, Mathew J

    2016-05-01

    As a measure of stroke severity, the National Institutes of Health Stroke Scale (NIHSS) is an important predictor of patient- and hospital-level outcomes, yet is often undocumented. The purpose of this study is to quantify and correct for potential selection bias in observed NIHSS data. Data were obtained from the Michigan Stroke Registry and included 10 262 patients with ischemic stroke aged ≥65 years discharged from 23 hospitals from 2009 to 2012, of which 74.6% of patients had documented NIHSS. We estimated models predicting NIHSS documentation and NIHSS score and used the Heckman selection model to estimate a correlation coefficient (ρ) between the 2 model error terms, which quantifies the degree of selection bias in the documentation of NIHSS. The Heckman model found modest, but significant, selection bias (ρ=0.19; 95% confidence interval: 0.09, 0.29; P2 points, which could significantly alter the risk profile of hospitals treating patients with ischemic stroke and subsequent hospital risk-adjusted outcomes. © 2016 American Heart Association, Inc.

  17. Sex-biased dispersal, kin selection and the evolution of sexual conflict.

    Science.gov (United States)

    Faria, Gonçalo S; Varela, Susana A M; Gardner, Andy

    2015-10-01

    There is growing interest in resolving the curious disconnect between the fields of kin selection and sexual selection. Rankin's (2011, J. Evol. Biol. 24, 71-81) theoretical study of the impact of kin selection on the evolution of sexual conflict in viscous populations has been particularly valuable in stimulating empirical research in this area. An important goal of that study was to understand the impact of sex-specific rates of dispersal upon the coevolution of male-harm and female-resistance behaviours. But the fitness functions derived in Rankin's study do not flow from his model's assumptions and, in particular, are not consistent with sex-biased dispersal. Here, we develop new fitness functions that do logically flow from the model's assumptions, to determine the impact of sex-specific patterns of dispersal on the evolution of sexual conflict. Although Rankin's study suggested that increasing male dispersal always promotes the evolution of male harm and that increasing female dispersal always inhibits the evolution of male harm, we find that the opposite can also be true, depending upon parameter values. © 2015 The Authors. Journal of Evolutionary Biology published by John Wiley & Sons Ltd on behalf of European Society for Evolutionary Biology.

  18. Social desirability bias in personality testing: Implications for astronaut selection

    Science.gov (United States)

    Sandal, Gro M.; Musson, Dave; Helmreich, Robert. L.; Gravdal, Lene

    2005-07-01

    The assessment of personality is recognized by space agencies as an approach to identify candidates likely to perform optimally during spaceflights. In the use of personality scales for selection, the impact of social desirability (SD) has been cited as a concern. Study 1 addressed the impact of SD on responses to the Personality Characteristic Inventory (PCI) and NEO-FFI. This was achieved by contrasting scores from active astronauts (N=65) with scores of successful astronaut applicants (N=63), and between pilots applicants (N=1271) and pilot research subjects (N=120). Secondly, personality scores were correlated with scores on the Marlow Crown Social Desirability Scale among applicants to managerial positions (N=120). The results indicated that SD inflated scores on PCI scales assessing negative interpersonal characteristics, and impacted on four of five scales in NEO-FFI. Still, the effect sizes were small or moderate. Study 2 addressed performance implications of SD during an assessment of males applying to work as rescue personnel operations in the North Sea (N=22). The results showed that SD correlated negatively with cognitive test performance, and positively with discrepancy in performance ratings between self and two observers. In conclusion, caution is needed in interpreting personality scores in applicant populations. SD may be a negative predictor for performance under stress.

  19. 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.

  20. Selection biases the prevalence and type of epistasis along adaptive trajectories.

    Science.gov (United States)

    Draghi, Jeremy A; Plotkin, Joshua B

    2013-11-01

    The contribution to an organism's phenotype from one genetic locus may depend upon the status of other loci. Such epistatic interactions among loci are now recognized as fundamental to shaping the process of adaptation in evolving populations. Although little is known about the structure of epistasis in most organisms, recent experiments with bacterial populations have concluded that antagonistic interactions abound and tend to deaccelerate the pace of adaptation over time. Here, we use the NK model of fitness landscapes to examine how natural selection biases the mutations that substitute during evolution based on their epistatic interactions. We find that, even when beneficial mutations are rare, these biases are strong and change substantially throughout the course of adaptation. In particular, epistasis is less prevalent than the neutral expectation early in adaptation and much more prevalent later, with a concomitant shift from predominantly antagonistic interactions early in adaptation to synergistic and sign epistasis later in adaptation. We observe the same patterns when reanalyzing data from a recent microbial evolution experiment. These results show that when the order of substitutions is not known, standard methods of analysis may suggest that epistasis retards adaptation when in fact it accelerates it. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

  1. 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%.

  2. 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.

  3. Translational Selection for Speed Is Not Sufficient to Explain Variation in Bacterial Codon Usage Bias

    Science.gov (United States)

    2018-01-01

    Abstract Increasing growth rate across bacteria strengthens selection for faster translation, concomitantly increasing the total number of tRNA genes and codon usage bias (CUB: enrichment of specific synonymous codons in highly expressed genes). Typically, enriched codons are translated by tRNAs with higher gene copy numbers (GCN). A model of tRNA–CUB coevolution based on fast growth-associated selection on translational speed recapitulates these patterns. A key untested implication of the coevolution model is that translational selection should favor higher tRNA GCN for more frequently used amino acids, potentially weakening the effect of growth-associated selection on CUB. Surprisingly, we find that CUB saturates with increasing growth rate across γ-proteobacteria, even as the number of tRNA genes continues to increase. As predicted, amino acid-specific tRNA GCN is positively correlated with the usage of corresponding amino acids, but there is no correlation between growth rate associated changes in CUB and amino acid usage. Instead, we find that some amino acids—cysteine and those in the NNA/G codon family—show weak CUB that does not increase with growth rate, despite large variation in the corresponding tRNA GCN. We suggest that amino acid-specific variation in CUB is not explained by tRNA GCN because GCN does not influence the difference between translation times of synonymous codons as expected. Thus, selection on translational speed alone cannot fully explain quantitative variation in overall or amino acid-specific CUB, suggesting a significant role for other functional constraints and amino acid-specific codon features. PMID:29385509

  4. Impact of translational selection on codon usage bias in the archaeon Methanococcus maripaludis

    Science.gov (United States)

    Emery, Laura R.; Sharp, Paul M.

    2011-01-01

    Patterns of codon usage have been extensively studied among Bacteria and Eukaryotes, but there has been little investigation of species from the third domain of life, the Archaea. Here, we examine the nature of codon usage bias in a methanogenic archaeon, Methanococcus maripaludis. Genome-wide patterns of codon usage are dominated by a strong A + T bias, presumably largely reflecting mutation patterns. Nevertheless, there is variation among genes in the use of a subset of putatively translationally optimal codons, which is strongly correlated with gene expression level. In comparison with Bacteria such as Escherichia coli, the strength of selected codon usage bias in highly expressed genes in M. maripaludis seems surprisingly high given its moderate growth rate. However, the pattern of selected codon usage differs between M. maripaludis and E. coli: in the archaeon, strongly selected codon usage bias is largely restricted to twofold degenerate amino acids (AAs). Weaker bias among the codons for fourfold degenerate AAs is consistent with the small number of tRNA genes in the M. maripaludis genome. PMID:20810428

  5. Impact of translational selection on codon usage bias in the archaeon Methanococcus maripaludis.

    Science.gov (United States)

    Emery, Laura R; Sharp, Paul M

    2011-02-23

    Patterns of codon usage have been extensively studied among Bacteria and Eukaryotes, but there has been little investigation of species from the third domain of life, the Archaea. Here, we examine the nature of codon usage bias in a methanogenic archaeon, Methanococcus maripaludis. Genome-wide patterns of codon usage are dominated by a strong A + T bias, presumably largely reflecting mutation patterns. Nevertheless, there is variation among genes in the use of a subset of putatively translationally optimal codons, which is strongly correlated with gene expression level. In comparison with Bacteria such as Escherichia coli, the strength of selected codon usage bias in highly expressed genes in M. maripaludis seems surprisingly high given its moderate growth rate. However, the pattern of selected codon usage differs between M. maripaludis and E. coli: in the archaeon, strongly selected codon usage bias is largely restricted to twofold degenerate amino acids (AAs). Weaker bias among the codons for fourfold degenerate AAs is consistent with the small number of tRNA genes in the M. maripaludis genome.

  6. 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.

  7. Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor.

    Science.gov (United States)

    Bridge, L J; Mead, J; Frattini, E; Winfield, I; Ladds, G

    2018-04-07

    Theoretical models of G protein-coupled receptor (GPCR) concentration-response relationships often assume an agonist producing a single functional response via a single active state of the receptor. These models have largely been analysed assuming steady-state conditions. There is now much experimental evidence to suggest that many GPCRs can exist in multiple receptor conformations and elicit numerous functional responses, with ligands having the potential to activate different signalling pathways to varying extents-a concept referred to as biased agonism, functional selectivity or pluri-dimensional efficacy. Moreover, recent experimental results indicate a clear possibility for time-dependent bias, whereby an agonist's bias with respect to different pathways may vary dynamically. Efforts towards understanding the implications of temporal bias by characterising and quantifying ligand effects on multiple pathways will clearly be aided by extending current equilibrium binding and biased activation models to include G protein activation dynamics. Here, we present a new model of time-dependent biased agonism, based on ordinary differential equations for multiple cubic ternary complex activation models with G protein cycle dynamics. This model allows simulation and analysis of multi-pathway activation bias dynamics at a single receptor for the first time, at the level of active G protein (α GTP ), towards the analysis of dynamic functional responses. The model is generally applicable to systems with N G G proteins and N* active receptor states. Numerical simulations for N G =N * =2 reveal new insights into the effects of system parameters (including cooperativities, and ligand and receptor concentrations) on bias dynamics, highlighting new phenomena including the dynamic inter-conversion of bias direction. Further, we fit this model to 'wet' experimental data for two competing G proteins (G i and G s ) that become activated upon stimulation of the adenosine A 1

  8. 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...

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

    Directory of Open Access Journals (Sweden)

    Mindy M Syfert

    Full Text Available 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.

  10. 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.

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

    Science.gov (United States)

    Baetz, Marilyn; Bowen, Rudy

    2014-01-01

    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. PMID:24688885

  12. 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.

  13. 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.

  14. 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

  15. 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 Impact factor: 3.760, year: 2016

  16. 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

  17. Appropriate microwave frequency selection for biasing superconducting hot electron bolometers as terahertz direct detectors

    Science.gov (United States)

    Jiang, S. L.; Li, X. F.; Jia, X. Q.; Kang, L.; Jin, B. B.; Xu, W. W.; Chen, J.; Wu, P. H.

    2017-04-01

    Terahertz (THz) direct detectors based on superconducting niobium nitride (NbN) hot electron bolometers (HEBs) and biased by a simple microwave (MW) source have been studied. The frequency and power of the MW are selected by measuring the MW responses of the current-voltage (I-V) curves and resistance-temperature (R-T) curves of the NbN HEBs. The non-uniform absorption theory is used to explain the current jumps in the I-V curves and the resistance jumps in the R-T curves. Compared to the thermal biasing, the MW biasing method can improve the sensitivity, make the readout system much easier and consumes less liquid helium, which is important for long lasting experiments. The noise equivalent power (NEP) of 1.6 pW Hz-1/2 and the response time of 86 ps are obtained for the detectors working at 4.2 K and 0.65 THz.

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

    Directory of Open Access Journals (Sweden)

    Thomas Pfeiffer

    2011-03-01

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

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. Selective attrition and bias in a longitudinal health survey among survivors of a disaster

    Directory of Open Access Journals (Sweden)

    Stellato Rebecca

    2007-02-01

    Full Text Available Abstract Background Little is known about the response mechanisms among survivors of disasters. We studied the selective attrition and possible bias in a longitudinal study among survivors of a fireworks disaster. Methods Survivors completed a questionnaire three weeks (wave 1, 18 months (wave 2 and four years post-disaster (wave 3. Demographic characteristics, disaster-related factors and health problems at wave 1 were compared between respondents and non-respondents at the follow-up surveys. Possible bias as a result of selective response was examined by comparing prevalence estimates resulting from multiple imputation and from complete case analysis. Analysis were stratified according to ethnic background (native Dutch and immigrant survivors. Results Among both native Dutch and immigrant survivors, female survivors and survivors in the age categories 25–44 and 45–64 years old were more likely to respond to the follow-up surveys. In general, disasters exposure did not differ between respondents and non-respondents at follow-up. Response at follow-up differed between native Dutch and non-western immigrant survivors. For example, native Dutch who responded only to wave 1 reported more depressive feelings at wave 1 (59.7%; 95% CI 51.2–68.2 than Dutch survivors who responded to all three waves (45.4%; 95% CI 41.6–49.2, p p Conclusion Our results indicate that despite selective response, the complete case prevalence estimates were only somewhat biased. Future studies, both among survivors of disasters and among the general population, should not only examine selective response, but should also investigate whether selective response has biased the complete case prevalence estimates of health problems by using statistical techniques such as multiple imputation.

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

    Directory of Open Access Journals (Sweden)

    Cole Wayant

    Full Text Available 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

  5. 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....

  6. 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

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

    Science.gov (United States)

    Yang, Rochelle; Carter, Barry L; Gums, Tyler H; Gryzlak, Brian M; Xu, Yinghui; Levy, Barcey T

    2017-07-10

    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. 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. 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, 1.92). Though there appeared to be a higher consent rate for females

  8. HIV transmission. Selection bias at the heterosexual HIV-1 transmission bottleneck.

    Science.gov (United States)

    Carlson, Jonathan M; Schaefer, Malinda; Monaco, Daniela C; Batorsky, Rebecca; Claiborne, Daniel T; Prince, Jessica; Deymier, Martin J; Ende, Zachary S; Klatt, Nichole R; DeZiel, Charles E; Lin, Tien-Ho; Peng, Jian; Seese, Aaron M; Shapiro, Roger; Frater, John; Ndung'u, Thumbi; Tang, Jianming; Goepfert, Paul; Gilmour, Jill; Price, Matt A; Kilembe, William; Heckerman, David; Goulder, Philip J R; Allen, Todd M; Allen, Susan; Hunter, Eric

    2014-07-11

    Heterosexual transmission of HIV-1 typically results in one genetic variant establishing systemic infection. We compared, for 137 linked transmission pairs, the amino acid sequences encoded by non-envelope genes of viruses in both partners and demonstrate a selection bias for transmission of residues that are predicted to confer increased in vivo fitness on viruses in the newly infected, immunologically naïve recipient. Although tempered by transmission risk factors, such as donor viral load, genital inflammation, and recipient gender, this selection bias provides an overall transmission advantage for viral quasispecies that are dominated by viruses with high in vivo fitness. Thus, preventative or therapeutic approaches that even marginally reduce viral fitness may lower the overall transmission rates and offer long-term benefits even upon successful transmission. Copyright © 2014, American Association for the Advancement of Science.

  9. Whom do children copy? Model-based biases in social learning

    OpenAIRE

    Wood, Lara Amanda Natalia; Kendal, Rachel; Flynn, Emma

    2013-01-01

    This review investigates the presence of young children‟s model-based cultural transmission biases in social learning, arguing that such biases are adaptive and flexible. Section 1 offers five propositions regarding the presence and direction of model-based transmission biases in young children's copying of a model. Section 2 discusses the cognitive abilities required for differing model-based biases and tracks their development in early childhood. Section 3 suggests future areas of research ...

  10. Tilted dipole model for bias-dependent photoluminescence pattern

    Science.gov (United States)

    Fujieda, Ichiro; Suzuki, Daisuke; Masuda, Taishi

    2014-12-01

    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.

  11. Tilted dipole model for bias-dependent photoluminescence pattern

    International Nuclear Information System (INIS)

    Fujieda, Ichiro; Suzuki, Daisuke; Masuda, Taishi

    2014-01-01

    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

  12. Evaluation of Bias Associated with Capture Maps Derived from Nonlinear Groundwater Flow Models.

    Science.gov (United States)

    Nadler, Cara; Allander, Kip; Pohll, Greg; Morway, Eric; Naranjo, Ramon; Huntington, Justin

    2017-09-21

    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. © 2017, National Ground Water Association.

  13. Biased selection of propagation-related TUPs from phage display peptide libraries.

    Science.gov (United States)

    Zade, Hesam Motaleb; Keshavarz, Reihaneh; Shekarabi, Hosna Sadat Zahed; Bakhshinejad, Babak

    2017-08-01

    Phage display is rapidly advancing as a screening strategy in drug discovery and drug delivery. Phage-encoded combinatorial peptide libraries can be screened through the affinity selection procedure of biopanning to find pharmaceutically relevant cell-specific ligands. However, the unwanted enrichment of target-unrelated peptides (TUPs) with no true affinity for the target presents an important barrier to the successful screening of phage display libraries. Propagation-related TUPs (Pr-TUPs) are an emerging but less-studied category of phage display-derived false-positive hits that are displayed on the surface of clones with faster propagation rates. Despite long regarded as an unbiased selection system, accumulating evidence suggests that biopanning may create biological bias toward selection of phage clones with certain displayed peptides. This bias can be dependent on or independent of the displayed sequence and may act as a major driving force for the isolation of fast-growing clones. Sequence-dependent bias is reflected by censorship or over-representation of some amino acids in the displayed peptide and sequence-independent bias is derived from either point mutations or rare recombination events occurring in the phage genome. It is of utmost interest to clean biopanning data by identifying and removing Pr-TUPs. Experimental and bioinformatic approaches can be exploited for Pr-TUP discovery. With no doubt, obtaining deeper insight into how Pr-TUPs emerge during biopanning and how they could be detected provides a basis for using cell-targeting peptides isolated from phage display screening in the development of disease-specific diagnostic and therapeutic platforms.

  14. Reward- and attention-related biasing of sensory selection in visual cortex.

    Science.gov (United States)

    Buschschulte, Antje; Boehler, Carsten N; Strumpf, Hendrik; Stoppel, Christian; Heinze, Hans-Jochen; Schoenfeld, Mircea A; Hopf, Jens-Max

    2014-05-01

    Attention to task-relevant features leads to a biasing of sensory selection in extrastriate cortex. Features signaling reward seem to produce a similar bias, but how modulatory effects due to reward and attention relate to each other is largely unexplored. To address this issue, it is critical to separate top-down settings defining reward relevance from those defining attention. To this end, we used a visual search paradigm in which the target's definition (attention to color) was dissociated from reward relevance by delivering monetary reward on search frames where a certain task-irrelevant color was combined with the target-defining color to form the target object. We assessed the state of neural biasing for the attended and reward-relevant color by analyzing the neuromagnetic brain response to asynchronously presented irrelevant distractor probes drawn in the target-defining color, the reward-relevant color, and a completely irrelevant color as a reference. We observed that for the prospect of moderate rewards, the target-defining color but not the reward-relevant color produced a selective enhancement of the neuromagnetic response between 180 and 280 msec in ventral extrastriate visual cortex. Increasing reward prospect caused a delayed attenuation (220-250 msec) of the response to reward probes, which followed a prior (160-180 msec) response enhancement in dorsal ACC. Notably, shorter latency responses in dorsal ACC were associated with stronger attenuation in extrastriate visual cortex. Finally, an analysis of the brain response to the search frames revealed that the presence of the reward-relevant color in search distractors elicited an enhanced response that was abolished after increasing reward size. The present data together indicate that when top-down definitions of reward relevance and attention are separated, the behavioral significance of reward-associated features is still rapidly coded in higher-level cortex areas, thereby commanding effective top

  15. Discharge simulations performed with a hydrological model using bias corrected regional climate model input

    Directory of Open Access Journals (Sweden)

    S. C. van Pelt

    2009-12-01

    Full Text Available Studies have demonstrated that precipitation on Northern Hemisphere mid-latitudes has increased in the last decades and that it is likely that this trend will continue. This will have an influence on discharge of the river Meuse. The use of bias correction methods is important when the effect of precipitation change on river discharge is studied. The objective of this paper is to investigate the effect of using two different bias correction methods on output from a Regional Climate Model (RCM simulation. In this study a Regional Atmospheric Climate Model (RACMO2 run is used, forced by ECHAM5/MPIOM under the condition of the SRES-A1B emission scenario, with a 25 km horizontal resolution. The RACMO2 runs contain a systematic precipitation bias on which two bias correction methods are applied. The first method corrects for the wet day fraction and wet day average (WD bias correction and the second method corrects for the mean and coefficient of variance (MV bias correction. The WD bias correction initially corrects well for the average, but it appears that too many successive precipitation days were removed with this correction. The second method performed less well on average bias correction, but the temporal precipitation pattern was better. Subsequently, the discharge was calculated by using RACMO2 output as forcing to the HBV-96 hydrological model. A large difference was found between the simulated discharge of the uncorrected RACMO2 run, the WD bias corrected run and the MV bias corrected run. These results show the importance of an appropriate bias correction.

  16. Genetic biases for showy males: are some genetic systems especially conducive to sexual selection?

    Science.gov (United States)

    Reeve, Hudson Kern; Pfennig, David W

    2003-02-04

    Male secondary sexual characters (conspicuous ornaments, signals, colors) are among nature's most striking features. Yet, it is unclear why certain groups of organisms are more likely than others to evolve these traits. One explanation for such taxonomic biases is that some genetic systems may be especially conducive to sexual selection. Here, we present theory and simulation results demonstrating that rare alleles encoding either male ornaments or female preferences for those ornaments are better protected against random loss in species with ZZZW or ZZZO sex chromosome systems (male homogamety) than in species with XXXY or XXXO systems (male heterogamety). Moreover, this protection is much stronger in diploid than haplodiploid species. We also present empirical data showing that male secondary sexual characters are better developed in diploid than haplodiploid species and in diploid species with male homogamety than in those with male heterogamety. Thus, taxonomic biases for showy males may stem from differences in sex chromosome systems.

  17. Varactor-tunable frequency selective surface with an embedded bias network

    International Nuclear Information System (INIS)

    Lin Bao-Qin; Zhang Heng-Yang; Li Wei; Qu Shao-Bo; Tong Chuang-Ming; Zhou Hang

    2013-01-01

    A new technique for designing a varactor-tunable frequency selective surface (FSS) with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

  18. 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

  19. 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.

  20. 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

  1. Multigenerational response to artificial selection for biased clutch sex ratios in Tigriopus californicus populations.

    Science.gov (United States)

    Alexander, H J; Richardson, J M L; Anholt, B R

    2014-09-01

    Polygenic sex determination (PSD) is relatively rare and theoretically evolutionary unstable, yet has been reported across a range of taxa. Evidence for multilocus PSD is provided by (i) large between-family variance in sex ratio, (ii) paternal and maternal effects on family sex ratio and (iii) response to selection for family sex ratio. This study tests the polygenic hypothesis of sex determination in the harpacticoid copepod Tigriopus californicus using the criterion of response to selection. We report the first multigenerational quantitative evidence that clutch sex ratio responds to artificial selection in both directions (selection for male- and female-biased families) and in multiple populations of T. californicus. In the five of six lines that showed a response to selection, realized heritability estimated by multigenerational analysis ranged from 0.24 to 0.58. Divergence of clutch sex ratio between selection lines is rapid, with response to selection detectable within the first four generations of selection. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

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

    Science.gov (United States)

    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.

  3. A model explaining the matrilateral bias in alloparental investment.

    Science.gov (United States)

    Perry, Gretchen; Daly, Martin

    2017-08-29

    Maternal grandmothers invest more in childcare than paternal grandmothers. This bias is large where the expression of preferences is unconstrained by residential and lineage norms, and is detectable even where marriage removes women from their natal families. We maintain that the standard evolutionary explanation, paternity uncertainty, is incomplete, and present an expanded model incorporating effects of alloparents on the mother as well as on her children. Alloparenting lightens a mother's load and increases her residual nepotistic value: her expected fitness from later investments in personal reproduction and in her natal relatives. The mother's mother derives fitness from all such investments, whereas her mother-in-law gains only from further investment in children sired by her son, and thus has less incentive to assist the mother even if paternity is certain. This logic extends to kin other than grandmothers. We generate several hypotheses for future research.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    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

  6. The effect of GCM biases on global runoff simulations of a land surface model

    Science.gov (United States)

    Papadimitriou, Lamprini V.; Koutroulis, Aristeidis G.; Grillakis, Manolis G.; Tsanis, Ioannis K.

    2017-09-01

    Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided

  7. Model biases in high-burnup fast reactor simulations

    International Nuclear Information System (INIS)

    Touran, N.; Cheatham, J.; Petroski, R.

    2012-01-01

    A new code system called the Advanced Reactor Modeling Interface (ARMI) has been developed that loosely couples multiscale, multiphysics nuclear reactor simulations to provide rapid, user-friendly, high-fidelity full systems analysis. Incorporating neutronic, thermal-hydraulic, safety/transient, fuel performance, core mechanical, and economic analyses, ARMI provides 'one-click' assessments of many multi-disciplined performance metrics and constraints that historically require iterations between many diverse experts. The capabilities of ARMI are implemented in this study to quantify neutronic biases of various modeling approximations typically made in fast reactor analysis at an equilibrium condition, after many repetitive shuffles. Sensitivities at equilibrium that result in very high discharge burnup are considered ( and >20% FIMA), as motivated by the development of the Traveling Wave Reactor. Model approximations discussed include homogenization, neutronic and depletion mesh resolution, thermal-hydraulic coupling, explicit control rod insertion, burnup-dependent cross sections, fission product model, burn chain truncation, and dynamic fuel performance. The sensitivities of these approximations on equilibrium discharge burnup, k eff , power density, delayed neutron fraction, and coolant temperature coefficient are discussed. (authors)

  8. 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).

  9. 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.

  10. A flexible genome-wide bootstrap method that accounts for ranking and threshold-selection bias in GWAS interpretation and replication study design.

    Science.gov (United States)

    Faye, Laura L; Sun, Lei; Dimitromanolakis, Apostolos; Bull, Shelley B

    2011-07-10

    The phenomenon known as the winner's curse is a form of selection bias that affects estimates of genetic association. In genome-wide association studies (GWAS) the bias is exacerbated by the use of stringent selection thresholds and ranking over hundreds of thousands of single nucleotide polymorphisms (SNPs). We develop an improved multi-locus bootstrap point estimate and confidence interval, which accounts for both ranking- and threshold-selection bias in the presence of genome-wide SNP linkage disequilibrium structure. The bootstrap method easily adapts to various study designs and alternative test statistics as well as complex SNP selection criteria. The latter is demonstrated by our application to the Wellcome Trust Case Control Consortium findings, in which the selection criterion was the minimum of the p-values for the additive and genotypic genetic effect models. In contrast, existing likelihood-based bias-reduced estimators account for the selection criterion applied to an SNP as if it were the only one tested, and so are more simple computationally, but do not address ranking across SNPs. Our simulation studies show that the bootstrap bias-reduced estimates are usually closer to the true genetic effect than the likelihood estimates and are less variable with a narrower confidence interval. Replication study sample size requirements computed from the bootstrap bias-reduced estimates are adequate 75-90 per cent of the time compared to 53-60 per cent of the time for the likelihood method. The bootstrap methods are implemented in a user-friendly package able to provide point and interval estimation for both binary and quantitative phenotypes in large-scale GWAS. Copyright © 2011 John Wiley & Sons, Ltd.

  11. 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.

  12. Models selection and fitting

    International Nuclear Information System (INIS)

    Martin Llorente, F.

    1990-01-01

    The models of atmospheric pollutants dispersion are based in mathematic algorithms that describe the transport, diffusion, elimination and chemical reactions of atmospheric contaminants. These models operate with data of contaminants emission and make an estimation of quality air in the area. This model can be applied to several aspects of atmospheric contamination

  13. Modeling Natural Selection

    Science.gov (United States)

    Bogiages, Christopher A.; Lotter, Christine

    2011-01-01

    In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…

  14. 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

  15. 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.

  16. Selected System Models

    Science.gov (United States)

    Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.

    Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.

  17. Selection bias in follow-up interviews with individuals attending the emergency department for occupational injuries

    DEFF Research Database (Denmark)

    Oesterlund, Anna H; Lander, Flemming; Rytter, Søren

    2017-01-01

    OBJECTIVE: To examine whether supplementary interview participation was comparable for occupationally injured patients attending two hospital emergency departments and to investigate the magnitude of selection bias in relation to sex, age, severity, job tasks and industry risk level. METHODS......: Workers aged 18-70 years who contacted the two emergency departments for an acute occupational injury in 2013 were eligible and given a short questionnaire. Following written consent, a semi-structured interview concerning health and transient risk factors was conducted by telephone. The two departments...... were compared for study recruitment by age and sex. Respondents and non-respondents to the interview were compared for age, sex, injury severity, job tasks and industry risk level. RESULTS: Of 4002 patients attending the two hospitals, 1693 (42%) participated in the interview. One hospital had...

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

    Directory of Open Access Journals (Sweden)

    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.

  19. The Success Of Stock Selection Strategies In Emerging Markets: Is It Risk Or Behavioral Bias?

    NARCIS (Netherlands)

    J. van der Hart; G.J. de Zwart (Gerben); D.J.C. van Dijk (Dick)

    2005-01-01

    textabstractWe examine competing explanations, based on risk and behavioral models, for the profitability of stock selection strategies in emerging markets. We document that both emerging market risk and global risk factors cannot account for the significant excess returns of selection strategies

  20. Intergenerational Transfer and Reporting Bias: An Application of the MIMIC Model.

    Science.gov (United States)

    Lin, I-Fen; Wu, Hsueh-Sheng

    2017-12-15

    Parents' and adult children's reports of transfer do not always agree, because each has respective bias. This study demonstrates a method to separate reporting bias from transfer and identify their respective correlates. The analysis was based on 4,947 parent-child dyads from the Family Roster and Transfer Module added to the 2013 wave of the Panel Study of Income Dynamics. Drawing on classical test theory, a multiple-indicators-and-multiple-causes (MIMIC) model was used to decompose parents' and adult children's reports of time and money transfers into a latent factor (true transfer) and unique factors (bias). This model further identified respective covariates associated with true transfer and bias. A substantial amount of bias existed in parents' and adult children's reports. The self-enhancement hypothesis did not fully explain how resources to help and need for support relate to the direction of reporting bias. Some correlates of transfer identified in prior studies were associated with transfer only, some with bias only, and others with both transfer and bias. Bias is common in both parents' and adult children's reports of transfer. Separating bias from transfer and identifying their respective correlates makes it possible to explain why intergenerational transfer and reporting bias occur. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  1. Biased small-molecule ligands for selective inhibition of HIV-1 cell entry via CCR5.

    Science.gov (United States)

    Berg, Christian; Spiess, Katja; Lüttichau, Hans R; Rosenkilde, Mette M

    2016-12-01

    Since the discovery of HIV's use of CCR5 as the primary coreceptor in fusion, the focus on developing small-molecule receptor antagonists for inhibition hereof has only resulted in one single drug, Maraviroc. We therefore investigated the possibility of using small-molecule CCR5 agonists as HIV-1 fusion inhibitors. A virus-free cell-based fusion reporter assay, based on mixing "effector cells" (expressing HIV Env and luciferase activator) with "target cells" (expressing CD4, CCR5 wild type or a selection of well-described mutations, and luciferase reporter), was used as fusion readout. Receptor expression was evaluated by ELISA and fluorescence microscopy. On CCR5 WT, Maraviroc and Aplaviroc inhibited fusion with high potencies (EC 50 values of 91 and 501 nM, respectively), whereas removal of key residues for both antagonists (Glu283Ala) or Maraviroc alone (Tyr251Ala) prevented fusion inhibition, establishing this assay as suitable for screening of HIV entry inhibitors. Both ligands inhibited HIV fusion on signaling-deficient CCR5 mutations (Tyr244Ala and Trp248Ala). Moreover, the steric hindrance CCR5 mutation (Gly286Phe) impaired fusion, presumably by a direct hindrance of gp120 interaction. Finally, the efficacy switch mutation (Leu203Phe) - converting small-molecule antagonists/inverse agonists to full agonists biased toward G-protein activation - uncovered that also small-molecule agonists can function as direct HIV-1 cell entry inhibitors. Importantly, no agonist-induced receptor internalization was observed for this mutation. Our studies of the pharmacodynamic requirements for HIV-1 fusion inhibitors highlight the possibility of future development of biased ligands with selective targeting of the HIV-CCR5 interaction without interfering with the normal functionality of CCR5.

  2. 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

  3. 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.

  4. SPICE Modeling of Body Bias Effect in 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 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.

  5. 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.

  6. A fractal model for the sea state bias in radar altimetry

    Directory of Open Access Journals (Sweden)

    D. E. Bar

    1997-01-01

    Full Text Available The Kirchhoff approximation is used to determine the sea state bias in radar altimetry. A weakly nonlinear model of the sea waves is used to derive the joint moments of two different points separated by a distance R; the bias moment is formulated, and found for power law spectra. The method provides a consistent analysis of the sea state bias and avoids the need to truncate the high frequency tail of power-law wave spectra. The model exhibits dependence of the "electromagnetic bias" on the radar frequency, an effect observed in field experiments.

  7. Plant-derived extracts in the neuroscience of anxiety on animal models: biases and comments.

    Science.gov (United States)

    Chirumbolo, Salvatore

    2012-04-01

    Generalized anxiety disorders probably represent one of the world's biggest mental health problems. A large number of studies have also shown that anxiety disorders and depression are often associated with quality of life impairments. As anxiety represents a big concern in public health, a substantial literature supports clinically important associations between psychiatric illness and chronic medical conditions. Actually, most research focuses on depression, finding that depression can adversely affect self-care and increase the risk of incident medical illness, complications, and mortality. Anxiety disorders are less well studied, but robust epidemiological and clinical evidences show that they play an equally important role. Recent reported articles have raised a debate about the effectiveness of some plant-derived extracts in anxiety-like models in mice. Biases about several aspects related with experimental setting, animal selection, environments, operators and investigators, selection and performance of behavioral tests, controls, results managing, and statistics are here discussed.

  8. 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

    to examine the null hypothesis that codon usage is due to mutation bias alone, not influenced by natural selection. Application of the test to the mammalian data led to rejection of the null hypothesis in most genes, suggesting that natural selection may be a driving force in the evolution of synonymous......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...

  9. 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.

  10. Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

    Directory of Open Access Journals (Sweden)

    Hongyin Xiang

    2016-01-01

    Full Text Available Most pixel-value-ordering (PVO predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

  11. Untold stories: biases and selection effects in research with victims of trafficking for sexual exploitation.

    Science.gov (United States)

    Brunovskis, Anette; Surtees, Rebecca

    2010-01-01

    Recent discussions of trafficking research have included calls for more innovative studies and new methodologies in order to move beyond the current trafficking narrative, which is often based on unrepresentative samples and overly simplified images. While new methods can potentially play a role in expanding the knowledge base on trafficking, this article argues that the solution is not entirely about applying new methods, but as much about using current methods to greater effect and with careful attention to their limitations and ethical constraints. Drawing on the authors' experience in researching trafficking issues in a number of projects over the past decade, the article outlines and exemplifies some of the methodological and ethical issues to be considered and accommodated when conducting research with trafficked persons -- including unrepresentative samples; access to respondents; selection biases by "gatekeepers" and self selection by potential respondents. Such considerations should inform not only how research is undertaken but also how this information is read and understood. Moreover, many of these considerations equally apply when considering the application of new methods within this field. The article maintains that a better understanding of how these issues come into play and inform trafficking research will translate into tools for conducting improved research in this field and, by implication, new perspectives on human trafficking.

  12. 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.

  13. Bias and Efficiency Tradeoffs in the Selection of Storm Suites Used to Estimate Flood Risk

    Directory of Open Access Journals (Sweden)

    Jordan R. Fischbach

    2016-02-01

    Full Text Available Modern joint probability methods for estimating storm surge or flood statistics are based on statistical aggregation of many hydrodynamic simulations that can be computationally expensive. Flood risk assessments that consider changing future conditions due to sea level rise or other drivers often require each storm to be run under a range of uncertain scenarios. Evaluating different flood risk mitigation measures, such as levees and floodwalls, in these future scenarios can further increase the computational cost. This study uses the Coastal Louisiana Risk Assessment model (CLARA to examine tradeoffs between the accuracy of estimated flood depth exceedances and the number and type of storms used to produce the estimates. Inclusion of lower-intensity, higher-frequency storms significantly reduces bias relative to storm suites with a similar number of storms but only containing high-intensity, lower-frequency storms, even when estimating exceedances at very low-frequency return periods.

  14. 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

  15. 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.

  16. Method for removing atomic-model bias in macromolecular crystallography

    Science.gov (United States)

    Terwilliger, Thomas C [Santa Fe, NM

    2006-08-01

    Structure factor bias in an electron density map for an unknown crystallographic structure is minimized by using information in a first electron density map to elicit expected structure factor information. Observed structure factor amplitudes are combined with a starting set of crystallographic phases to form a first set of structure factors. A first electron density map is then derived and features of the first electron density map are identified to obtain expected distributions of electron density. Crystallographic phase probability distributions are established for possible crystallographic phases of reflection k, and the process is repeated as k is indexed through all of the plurality of reflections. An updated electron density map is derived from the crystallographic phase probability distributions for each one of the reflections. The entire process is then iterated to obtain a final set of crystallographic phases with minimum bias from known electron density maps.

  17. 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.

  18. [Analysis of selection bias in the pilot study of a longitudinal study on aging in Spain].

    Science.gov (United States)

    Rodríguez Laso, Ángel; Urdaneta Artola, Elena; de la Fuente Sánchez, Miguel; Galindo Moreno, Eva; Yanguas Lezáun, José Javier; Rodríguez Rodríguez, Vicente

    2013-01-01

    To demonstrate that selection of a probabilistic sample at a national level for a study of aging in Spain is subject to selection bias. To quantify the losses produced after each phase of the administration of a questionnaire. We performed a cross-sectional study of the Spanish community-dwelling population aged 50 years or older between 2010 and 2011. Through multivariate logistic regressions, the characteristics of the census tract of the patients' residence were compared between those who agreed (n = 5,813) or refused (n = 7,023) to be included in the sampling frame and between those who agreed (n = 1,677) or refused (n = 2,875) to participate in the study. The individual characteristics of persons who responded (n = 1,398) or refused to respond (n = 346) to a face-to-face questionnaire administered after a telephone interview were also compared. In addition, the reasons for refusal were studied. The most frequent specific reasons for refusing to be included in the sampling frame or to participate in the study were poor health and disability (14.4% and 27.9%, respectively). In both cases, refusal was more frequent in the census tracts of districts with a lower socioeconomic level or those located in Catalonia, Guipúzcoa or Biscay. Individuals older than 81 participated less frequently in the face-to-face questionnaire. Between 8.6% and 18.4% of participants were lost at each stage of information retrieval. Probabilistic sampling in sampling points chosen by the researchers would allow more resources to be devoted to increasing response rates among the groups who are less likely to participate. Questions should be concentrated in only one shorter questionnaire, administered before blood extraction. Copyright © 2012 SESPAS. Published by Elsevier Espana. All rights reserved.

  19. 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.

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

    Science.gov (United States)

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

    2018-02-01

    The evolution of the linear and scale independent bias, based on the most popular dark matter bias models within the 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 × 1012h-1M⊙ and 1.4 × 1013h-1M⊙, for the different bias models. Similar results, reaching however a maximum value of ˜2 × 1013h-1M⊙, 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.

  1. 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.

  2. 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

  3. Model bias for South Atlantic Antarctic intermediate water in CMIP5

    Science.gov (United States)

    Zhu, Chenyu; Liu, Zhengyu; Gu, Sifan

    2017-07-01

    Characterized by a salinity minimum in the mid-depth, the Antarctic Intermediate Water (AAIW) is an important component of global ocean water mass. The simulation of the AAIW in current climate models, however, has remained deficient, especially in the Atlantic sector. Here, we evaluate the simulation of the South Atlantic AAIW in eleven state-of-the-art coupled climate models. It is found that all the models show a common AAIW bias relative to the observation, with a saltier, warmer and lighter core located at a shallower depth. This AAIW bias seems to contribute to a deficient freshwater export by the Atlantic Meridional Overturning Circulation (AMOC), potentially overstabilizing the AMOC. The causes of the bias are investigated in sensitivity experiments using an ocean alone model. It is found that the AAIW bias is caused neither by the surface climate bias nor the North Atlantic bias, although is weakly affected by the inter-basin exchange. This left the conclusion that the AAIW bias is caused predominantly by the deficient model representation of ocean dynamics and mixing processes in the AAIW region.

  4. 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.

  5. scnRCA: a novel method to detect consistent patterns of translational selection in mutationally-biased genomes.

    Directory of Open Access Journals (Sweden)

    Patrick K O'Neill

    Full Text Available Codon usage bias (CUB results from the complex interplay between translational selection and mutational biases. Current methods for CUB analysis apply heuristics to integrate both components, limiting the depth and scope of CUB analysis as a technique to probe into the evolution and optimization of protein-coding genes. Here we introduce a self-consistent CUB index (scnRCA that incorporates implicit correction for mutational biases, facilitating exploration of the translational selection component of CUB. We validate this technique using gene expression data and we apply it to a detailed analysis of CUB in the Pseudomonadales. Our results illustrate how the selective enrichment of specific codons among highly expressed genes is preserved in the context of genome-wide shifts in codon frequencies, and how the balance between mutational and translational biases leads to varying definitions of codon optimality. We extend this analysis to other moderate and fast growing bacteria and we provide unified support for the hypothesis that C- and A-ending codons of two-box amino acids, and the U-ending codons of four-box amino acids, are systematically enriched among highly expressed genes across bacteria. The use of an unbiased estimator of CUB allows us to report for the first time that the signature of translational selection is strongly conserved in the Pseudomonadales in spite of drastic changes in genome composition, and extends well beyond the core set of highly optimized genes in each genome. We generalize these results to other moderate and fast growing bacteria, hinting at selection for a universal pattern of gene expression that is conserved and detectable in conserved patterns of codon usage bias.

  6. 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

  7. 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)

  8. 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.

  9. Pangenome Evidence for Higher Codon Usage Bias and Stronger Translational Selection in Core Genes of Escherichia coli.

    Science.gov (United States)

    Sun, Shixiang; Xiao, Jingfa; Zhang, Huiyong; Zhang, Zhang

    2016-01-01

    Codon usage bias, as a combined interplay from mutation and selection, has been intensively studied in Escherichia coli. However, codon usage analysis in an E. coli pangenome remains unexplored and the relative importance of mutation and selection acting on core genes and strain-specific genes is unknown. Here we perform comprehensive codon usage analyses based on a collection of multiple complete genome sequences of E. coli. Our results show that core genes that are present in all strains have higher codon usage bias than strain-specific genes that are unique to single strains. We further explore the forces in influencing codon usage and investigate the difference of the major force between core and strain-specific genes. Our results demonstrate that although mutation may exert genome-wide influences on codon usage acting similarly in different gene sets, selection dominates as an important force to shape biased codon usage as genes are present in an increased number of strains. Together, our results provide important insights for better understanding genome plasticity and complexity as well as evolutionary mechanisms behind codon usage bias.

  10. 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

  11. Do Females with Bulimia Nervosa and Eating Disorder Not Otherwise Specified Have Selective Memory Biases?

    Science.gov (United States)

    Griffith, Emma; Kuyken, Willem; Watkins, Ed; Jones, Alysun

    2015-09-01

    The cognitive model suggests memory biases for weight/shape and food related information could be important in the maintenance of eating disorders. The current study aims to evaluate this and extend previous research by (a) including females with eating disorder not otherwise specified (EDNOS) as a discreet group; (b) considering whether levels of hunger and the pleasantness of the stimulus words are important in word recall. The study includes three groups of females, 16 with bulimia nervosa, 18 with EDNOS and 17 non-dieting general population controls. All participants completed a self-referential encoding and memory recall task. A main effect of word type (p eating disorder groups recalled significantly more weight/shape and food words compared to all other word categories (p eating disorder groups. In relation to the recall of food words, no significant differences were found between groups for levels of hunger. Both eating disorder groups rated the negative weight/shape (p < .01), negative food (p < .01) and neutral body words (p < .01) as more unpleasant than the control group. The implications for cognitive theory and future research are discussed.

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

    Directory of Open Access Journals (Sweden)

    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

  13. Regional climate models reduce biases of global models and project smaller European summer warming

    Science.gov (United States)

    Soerland, S.; Schar, C.; Lüthi, D.; Kjellstrom, E.

    2017-12-01

    The assessment of regional climate change and the associated planning of adaptation and response strategies are often based on complex model chains. Typically, these model chains employ global and regional climate models (GCMs and RCMs), as well as one or several impact models. It is a common belief that the errors in such model chains behave approximately additive, thus the uncertainty should increase with each modeling step. If this hypothesis were true, the application of RCMs would not lead to any intrinsic improvement (beyond higher-resolution detail) of the GCM results. Here, we investigate the bias patterns (offset during the historical period against observations) and climate change signals of two RCMs that have downscaled a comprehensive set of GCMs following the EURO-CORDEX framework. The two RCMs reduce the biases of the driving GCMs, reduce the spread and modify the amplitude of the GCM projected climate change signal. The GCM projected summer warming at the end of the century is substantially reduced by both RCMs. These results are important, as the projected summer warming and its likely impact on the water cycle are among the most serious concerns regarding European climate change.

  14. 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.

  15. 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

    that are able to block the human receptor. Docking of exemplar antagonists from two chemical series to homology models of both human and mouse Free Fatty Acid Receptor 2 suggested that a single lysine - arginine variation at the extracellular face of the receptor might provide the basis for antagonist...... 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...

  16. Biases in simulation of the rice phenology models when applied in warmer climates

    Science.gov (United States)

    Zhang, T.; Li, T.; Yang, X.; Simelton, E.

    2015-12-01

    The current model inter-comparison studies highlight the difference in projections between crop models when they are applied to warmer climates, but these studies do not provide results on how the accuracy of the models would change in these projections because the adequate observations under largely diverse growing season temperature (GST) are often unavailable. Here, we investigate the potential changes in the accuracy of rice phenology models when these models were applied to a significantly warmer climate. We collected phenology data from 775 trials with 19 cultivars in 5 Asian countries (China, India, Philippines, Bangladesh and Thailand). Each cultivar encompasses the phenology observations under diverse GST regimes. For a given rice cultivar in different trials, the GST difference reaches 2.2 to 8.2°C, which allows us to calibrate the models under lower GST and validate under higher GST (i.e., warmer climates). Four common phenology models representing major algorithms on simulations of rice phenology, and three model calibration experiments were conducted. The results suggest that the bilinear and beta models resulted in gradually increasing phenology bias (Figure) and double yield bias per percent increase in phenology bias, whereas the growing-degree-day (GDD) and exponential models maintained a comparatively constant bias when applied in warmer climates (Figure). Moreover, the bias of phenology estimated by the bilinear and beta models did not reduce with increase in GST when all data were used to calibrate models. These suggest that variations in phenology bias are primarily attributed to intrinsic properties of the respective phenology model rather than on the calibration dataset. Therefore we conclude that using the GDD and exponential models has more chances of predicting rice phenology correctly and thus, production under warmer climates, and result in effective agricultural strategic adaptation to and mitigation of climate change.

  17. N3 Bias Field Correction Explained as a Bayesian Modeling Method

    DEFF Research Database (Denmark)

    Larsen, Christian Thode; Iglesias, Juan Eugenio; Van Leemput, Koen

    2014-01-01

    Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function....... In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computational strategies as expectation maximization (EM) based bias field correction methods. We demonstrate experimentally that purely EM-based methods are capable...

  18. Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems

    Directory of Open Access Journals (Sweden)

    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.

  19. Wind models for the NSTS ascent trajectory biasing for wind load alleviation

    Science.gov (United States)

    Smith, O. E.; Adelfang, S. I.; Batts, G. W.

    1990-01-01

    New concepts are presented for aerospace vehicle ascent wind profile biasing. The purpose for wind biasing the ascent trajectory is to provide ascent wind loads relief and thus decrease the probability for launch delays due to wind loads exceeding critical limits. Wind biasing trajectories to the the profile of monthly mean winds have been widely used for this purpose. The wind profile models presented give additional alternatives for wind biased trajectories. They are derived from the properties of the bivariate normal probability function using the available wind statistical parameters for the launch site. The analytical expressions are presented to permit generalizations. Specific examples are given to illustrate the procedures. The wind profile models can be used to establish the ascent trajectory steering commands to guide the vehicle through the first stage. For the National Space Transportation System (NSTS) program these steering commands are called I-loads.

  20. Influence of Referral Pathway on Ebola Virus Disease Case-Fatality Rate and Effect of Survival Selection Bias.

    Science.gov (United States)

    Rudolf, Frauke; Damkjær, Mads; Lunding, Suzanne; Dornonville de la Cour, Kenn; Young, Alyssa; Brooks, Tim; Sesay, Tom; Salam, Alex P; Mishra, Sharmistha; Storgaard, Merete

    2017-04-01

    Case-fatality rates in Ebola treatment centers (ETCs) varied widely during the Ebola virus disease (EVD) outbreak in West Africa. We assessed the influence of referral pathway on ETC case-fatality rates with a retrospective cohort of 126 patients treated at the Mathaska ETC in Port Loko, Sierra Leone. The patients consisted of persons who had confirmed EVD when transferred to the ETC or who had been diagnosed onsite. The case-fatality rate for transferred patients was 46% versus 67% for patients diagnosed onsite (p = 0.02). The difference was mediated by Ebola viral load at diagnosis, suggesting a survival selection bias. Comparisons of case-fatality rates across ETCs and clinical management strategies should account for potential survival selection bias.

  1. System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering

    Directory of Open Access Journals (Sweden)

    Zebo Zhou

    2016-01-01

    Full Text Available In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-to-noise ratio are both computed for evaluating the internal and external reliability of overall system, respectively.

  2. 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...

  3. Voter models with heterozygosity selection

    Czech Academy of Sciences Publication Activity Database

    Sturm, A.; Swart, Jan M.

    2008-01-01

    Roč. 18, č. 1 (2008), s. 59-99 ISSN 1050-5164 R&D Projects: GA ČR GA201/06/1323; GA ČR GA201/07/0237 Institutional research plan: CEZ:AV0Z10750506 Keywords : Heterozygosity selection * rebellious voter model * branching * annihilation * survival * coexistence Subject RIV: BA - General Mathematics Impact factor: 1.285, year: 2008

  4. Use of Paired Simple and Complex Models to Reduce Predictive Bias and Quantify Uncertainty

    DEFF Research Database (Denmark)

    Doherty, John; Christensen, Steen

    2011-01-01

    into the costs of model simplification, and into how some of these costs may be reduced. It then describes a methodology for paired model usage through which predictive bias of a simplified model can be detected and corrected, and postcalibration predictive uncertainty can be quantified. The methodology...

  5. 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.

  6. Mitigating Bias in Generalized Linear Mixed Models: The Case for Bayesian Nonparametrics.

    Science.gov (United States)

    Antonelli, Joseph; Trippa, Lorenzo; Haneuse, Sebastien

    2016-02-01

    Generalized linear mixed models are a common statistical tool for the analysis of clustered or longitudinal data where correlation is accounted for through cluster-specific random effects. In practice, the distribution of the random effects is typically taken to be a Normal distribution, although if this does not hold then the model is misspecified and standard estimation/inference may be invalid. An alternative is to perform a so-called nonparametric Bayesian analyses in which one assigns a Dirichlet process (DP) prior to the unknown distribution of the random effects. In this paper we examine operating characteristics for estimation of fixed effects and random effects based on such an analysis under a range of "true" random effects distributions. As part of this we investigate various approaches for selection of the precision parameter of the DP prior. In addition, we illustrate the use of the methods with an analysis of post-operative complications among n = 18, 643 female Medicare beneficiaries who underwent a hysterectomy procedure at N = 503 hospitals in the US. Overall, we conclude that using the DP priori n modeling the random effect distribution results in large reductions of bias with little loss of efficiency. While no single choice for the precision parameter will be optimal in all settings, certain strategies such as importance sampling or empirical Bayes can be used to obtain reasonable results in a broad range of data scenarios.

  7. 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....

  8. Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.

    Science.gov (United States)

    van Walraven, Carl

    2017-04-01

    Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias. Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates. Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized. Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. 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.

  10. Domain-general biases in spatial localization: Evidence against a distorted body model hypothesis.

    Science.gov (United States)

    Medina, Jared; Duckett, Caitlin

    2017-07-01

    A number of studies have proposed the existence of a distorted body model of the hand. Supporting this hypothesis, judgments of the location of hand landmarks without vision are characterized by consistent distortions-wider knuckle and shorter finger lengths. We examined an alternative hypothesis in which these biases are caused by domain-general mechanisms, in which participants overestimate the distance between consecutive localization judgments that are spatially close. To do so, we examined performance on a landmark localization task with the hand (Experiments 1-3) using a lag-1 analysis. We replicated the widened knuckle judgments in previous studies. Using the lag-1 analysis, we found evidence for a constant overestimation bias along the mediolateral hand axis, such that consecutive stimuli were perceived as farther apart when they were closer (e.g., index-middle knuckle) versus farther (index-pinky) in space. Controlling for this bias, we found no evidence for a distorted body model along the mediolateral hand axis. To examine whether similar widening biases could be found with noncorporeal stimuli, we asked participants to localize remembered dots on a hand-like array (Experiments 4-5). Mean localization judgments were wider than actual along the primary array axis, similar to previous work with hands. As with proprioceptively defined stimuli, we found that this widening was primarily due to a constant overestimation bias. These results provide substantial evidence against a distorted body model hypothesis and support a domain-general model in which responses are biased away from the uncertainty distribution of the previous trial, leading to a constant overestimation bias. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  11. Selective memory bias for self-threatening memories in trait anxiety.

    Science.gov (United States)

    Saunders, Jo

    2013-01-01

    Previous research has suggested that we tend to forget information that is self-threatening--an effect known as mnemic neglect. Three experiments are reported, which examined mnemic neglect in anxiety and whether high-anxious individuals show facilitated memory for self-threatening material. In Experiment 1, high-anxious participants were found to have facilitated memory for self-threatening information in comparison to low-anxious participants. In Experiments 2 and 3 boundary conditions to this memory bias for self-threatening memories were examined, which revealed facilitated recall of self-threatening memories when this information was unmodifiable (Experiment 2) and when this information was highly diagnostic of underlying traits (Experiment 3). The findings indicate that high-anxious participants show reversed mnemic neglect effects indicating increased access to self-threatening information. The findings suggest that high-anxious individuals do show memory bias for threatening information but only under certain circumstances.

  12. A statistical bias correction for climate model data: parameter sensitivity analysis.

    Science.gov (United States)

    Piani, C.; Coppola, E.; Mariotti, L.; Haerter, J.; Hagemann, S.

    2009-04-01

    Water management adaptation strategies depend crucially on high quality projections of the hydrological cycle in view of anthropogenic climate change. The goodness of hydrological cycle projections depends, in turn, on the successful coupling of hydrological models to global (GCMs) or regional climate models (RCMs). It is well known within the climate modelling community that hydrological forcing output from climate models, in particular precipitation, is partially affected by large bias. The bias affects all aspects of the statistics, that is the mean, standard deviation (variability), skewness (drizzle versus intense events, dry days) etc. The state-of-the-art approach to bias correction is based on histogram equalization techniques. Such techniques intrinsically correct all moments of the statistical intensity distribution. However these methods are applicable to hydrological projections to the extent that the correction itself is robust, that is, defined by few parameters that are well constrained by available data and constant in time. Here we present details of the statistical bias correction methodology developed within the European project "Water and Global Change" (WATCH). We will suggest different versions of the method that allow it to be taylored to differently structured biases from different RCMs. Crucially, application of the methodology also allows for a sensitivity analysis of the correction parameters on other gridded variables such as orography and land use. Here we explore some of these sensitivities as well.

  13. Perception of facial expressions reveals selective affect-biased attention in humans and orangutans.

    Science.gov (United States)

    Pritsch, Carla; Telkemeyer, Silke; Mühlenbeck, Cordelia; Liebal, Katja

    2017-08-10

    Rapid detection and recognition of another individual's emotional state plays a pivotal role for humans and, most likely, other social species. Proper reactions contribute to an individual's survival in potentially dangerous situations; this is ensured by a preferential attention towards salient cues. The predisposition to attend to certain categories of affectively salient stimuli- also referred to as affect-biased attention - is likely shared with other species, since fast detection of and appropriate reaction to threats is crucial to survival. We compared human children and one of our close relatives, Sumatran orangutans (Pongo abelii), and predicted that both look more attentively and longer at emotionally salient facial expressions of their own and corresponding other species, compared to neutral faces. However, in contrast to a bias towards emotions providing relevant information by indicating a threat, both species preferentially looked at the fear-related, but not the angry faces of humans and consistently preferred the silent-bared teeth espressions in orangutans. The differential attention towards certain expressions might derive from their social function and the need to detect a potential threat in the environment. Our findings are consistent with claims rooting this affect-biased attention characteristic of human perception in our evolutionary history.

  14. 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.

  15. Kinematic bias on centrality selection of jet events in pPb collisions at the LHC

    Energy Technology Data Exchange (ETDEWEB)

    Armesto, Néstor, E-mail: nestor.armesto@usc.es [Departamento de Física de Partículas and IGFAE, Universidade de Santiago de Compostela, E-15706 Santiago de Compostela, Galicia (Spain); Gülhan, Doğa Can, E-mail: dgulhan@mit.edu [Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139 (United States); Milhano, José Guilherme, E-mail: guilherme.milhano@tecnico.ulisboa.pt [CENTRA, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, P-1049-001 Lisboa (Portugal); Physics Department, Theory Unit, CERN, CH-1211 Genève 23 (Switzerland)

    2015-07-30

    Centrality selection has been observed to have a large effect on jet observables in pPb collisions at the Large Hadron Collider, stronger than that predicted by the nuclear modification of parton densities. We study to which extent simple considerations of energy–momentum conservation which link the hard process with the underlying event that provides the centrality estimator, affect jets observables in such collisions. We develop a simplistic approach that considers first the production of jets in a pp collision as described by PYTHIA. From each pp collision, the value of the energy of the parton from the proton participating in the hard scattering is extracted. Then, the underlying event is generated simulating a pPb collision through HIJING, but with the energy of the proton decreased according to the value extracted in the previous step, and both collisions are added. This model is able to capture the bulk of the centrality effect for central to semicentral collisions, for the two available sets of data: dijets from the CMS Collaboration and single jets from the ATLAS Collaboration. As expected, the model fails for peripheral collisions where very few nucleons from Pb participate.

  16. Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1

    Science.gov (United States)

    Woelfle, M. D.; Yu, S.; Bretherton, C. S.; Pritchard, M. S.

    2018-01-01

    Six month coupled hindcasts show the central equatorial Pacific cold tongue bias development in a GCM to be sensitive to the atmospheric convective parameterization employed. Simulations using the standard configuration of the Community Earth System Model version 1 (CESM1) develop a cold bias in equatorial Pacific sea surface temperatures (SSTs) within the first two months of integration due to anomalous ocean advection driven by overly strong easterly surface wind stress along the equator. Disabling the deep convection parameterization enhances the zonal pressure gradient leading to stronger zonal wind stress and a stronger equatorial SST bias, highlighting the role of pressure gradients in determining the strength of the cold bias. Superparameterized hindcasts show reduced SST bias in the cold tongue region due to a reduction in surface easterlies despite simulating an excessively strong low-level jet at 1-1.5 km elevation. This reflects inadequate vertical mixing of zonal momentum from the absence of convective momentum transport in the superparameterized model. Standard CESM1simulations modified to omit shallow convective momentum transport reproduce the superparameterized low-level wind bias and associated equatorial SST pattern. Further superparameterized simulations using a three-dimensional cloud resolving model capable of producing realistic momentum transport simulate a cold tongue similar to the default CESM1. These findings imply convective momentum fluxes may be an underappreciated mechanism for controlling the strength of the equatorial cold tongue. Despite the sensitivity of equatorial SST to these changes in convective parameterization, the east Pacific double-Intertropical Convergence Zone rainfall bias persists in all simulations presented in this study.

  17. 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....

  18. Incorporating affective bias in models of human decision making

    Science.gov (United States)

    Nygren, Thomas E.

    1991-01-01

    Research on human decision making has traditionally focused on how people actually make decisions, how good their decisions are, and how their decisions can be improved. Recent research suggests that this model is inadequate. Affective as well as cognitive components drive the way information about relevant outcomes and events is perceived, integrated, and used in the decision making process. The affective components include how the individual frames outcomes as good or bad, whether the individual anticipates regret in a decision situation, the affective mood state of the individual, and the psychological stress level anticipated or experienced in the decision situation. A focus of the current work has been to propose empirical studies that will attempt to examine in more detail the relationships between the latter two critical affective influences (mood state and stress) on decision making behavior.

  19. Isolating the effect of major depression on obesity: role of selection bias.

    Science.gov (United States)

    Dave, Dhaval M; Tennant, Jennifer; Colman, Gregory

    2011-12-01

    There is suggestive evidence that rates of major depression have risen markedly in the U.S. concurrent with the rise in obesity. The economic burden of depression, about USD100 billion annually, is under-estimated if depression has a positive causal impact on obesity. However, virtually the entire existing literature on the connection between the two conditions has examined merely whether they are significantly correlated, sometimes holding constant a limited set of demographic factors. This study assesses whether, and the extent to which, the positive association between the two conditions reflects a causal link from major depression to higher BMI and obesity. Individual-level data from three nationally-representative studies are utilized: (i) National Comorbidity Survey-Replication (N=3,229); (ii) National Longitudinal Survey of Youth-1979 (N=21,365); and (iii) Behavioral Risk Factor Surveillance System (N=2,858,973). Dependent variables include body mass index (BMI) and a dichotomous indicator for overweight or obese. We measure diagnosed major depression based on DSM-IV criteria and the CES Depression scale. While contemporaneous effects are considered, the study primarily focuses on the effects of past and lifetime depression to bypass reverse causality and further assess the role of non-random selection on unobservable factors. The effects of past and lifetime depression on obesity are estimated based on: (i) models that control for an extensive set of typically-unobserved factors, including parental history, family background, parental investments, risk-taking, and use of anti-depressants and other prescription medications; (ii) constrained selection models; and (iii) models controlling for family fixed effects. There are expectedly no significant or substantial effects of current depression on BMI or overweight/obesity, given that BMI is a stock that changes relatively slowly over time. Results also do not support a causal interpretation among males

  20. A Multi-model Study on Warm Precipitation Biases in Global Models Compared to Satellite Observations

    Science.gov (United States)

    Jing, X.; Suzuki, K.; Guo, H.; Goto, D.; Ogura, T.; Koshiro, T.; Mulmenstadt, J.

    2017-12-01

    The cloud-to-precipitation transition process in warm clouds simulated by state-of-the-art global climate models (GCMs), including both traditional climate models and a global cloud-resolving model, is evaluated against A-Train satellites observations. The models and satellite observations are compared in the form of the statistics obtained from combined analysis of multiple satellite observables that probe signatures of the cloud-to-precipitation transition process. One common problem identified among these models is the too frequent occurrence of warm precipitation. The precipitation is found to form when the cloud particle size and the liquid water path (LWP) are both much smaller than those in observations. The too efficient formation of precipitation is found to be compensated for by errors of cloud microphysical properties, such as underestimated cloud particle size and LWP, to an extent that varies among the models. However, this does not completely cancel the precipitation formation bias. Robust errors are also found in the evolution of cloud microphysical properties in precipitation process in some GCMs, implying unrealistic interaction between precipitation and cloud water. Nevertheless, auspicious information is found for future improvement of warm precipitation representations: the adoption of more realistic autoconversion scheme or subgrid variability scheme is shown to improve the triggering of precipitation and evolution of cloud microphysical properties.

  1. Quantifying Arctic Cloud Biases in Global Models Using Active Satellite Observations

    Science.gov (United States)

    L'Ecuyer, T. S.; Sledd, A.; Mateling, M.; Schlegel, N.; Christensen, M.

    2017-12-01

    Observations from active sensors aboard polar orbiting satellites have yielded valuable new insights into the character and impacts of clouds and precipitation in polar regions. For example, recent analyses of CloudSat and CALIPSO datasets have quantified the influence of clouds on ice sheet energy balance and confirmed the importance of super-cooled liquid clouds in modulating surface melt processes. Perhaps more importantly, new observation-based reconstructions of the Arctic energy budget and its annual cycle have revealed that reanalyses and climate models exhibit significant biases in several key energy flows in the region. These biases, in turn, lead to discrepancies in the seasonal cycle of implied surface heat storage and energy transport into the Arctic from lower latitudes. To begin to diagnose possible sources of these biases, a new multi-satellite, multi-model combined Arctic dataset will be used to explore variability in the Arctic cloud radiative effects associated with changes in sea ice and snow cover.

  2. A Subhalo-Galaxy Correspondence Model of Galaxy Biasing

    Science.gov (United States)

    Kim, Juhan; Park, Changbom; Choi, Yun-Young

    2008-08-01

    We propose a model for allocating galaxies in cosmological N-body simulations. We identify each subhalo with a galaxy and assign luminosity and morphological type, assuming that the galaxy luminosity is a monotonic function of the host subhalo mass. Morphology is assigned using two simple relations between the subhalo mass and galaxy luminosity for different galaxy types. The first uses a constant luminosity ratio between early-type (E/SO) and late-type (S/Irr) galaxies at a fixed subhalo mass. The other assumes that galaxies of different morphological types but equal luminosity have a constant ratio of subhalo mass. We made a series of comparisons of the properties of these mock galaxies with those of SDSS galaxies. The resulting mock galaxy sample is found to successfully reproduce the observed local number density distribution except in high-density regions. We study the luminosity function as a function of local density, and find that the observed luminosity functions in different local density environments are overall well reproduced by the mock galaxies. A discrepancy is found at the bright end of the luminosity function of early types in the underdense regions and at the faint end of both morphological types in very high density regions. A significant fraction of the observed early-type galaxies in voids seem to have undergone relatively recent star formation and become brighter. The lack of faint mock galaxies in dense regions may be due to the strong tidal force of the central halo, which destroys less massive satellite subhalos around the simulation. The mass-to-light ratio is found to depend on the local density in a way similar to that observed in the SDSS sample. We have found an impressive agreement between our mock galaxies and the SDSS galaxies in the dependence of central velocity dispersion on the local density and luminosity.

  3. HESS Opinions "Should we apply bias correction to global and regional climate model data?"

    Directory of Open Access Journals (Sweden)

    J. Liebert

    2012-09-01

    Full Text Available Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with

  4. Kinematic biases on centrality selection of jet events in pPb collisions at the LHC

    CERN Document Server

    Armesto, Nestor; Milhano, Jose Guilherme

    2015-01-01

    Centrality selection has been observed to have a large effect on jet observables in pPb collisions at the Large Hadron Collider, stronger than that predicted by the nuclear modification of parton densities. We study to which extent simple considerations of energy-momentum conservation between the hard process and the underlying event affect jets observables in such collisions. We develop a simplistic approach that considers first the production of jets in a pp collision as described by PYTHIA. From each pp collision, the value of the energy of the parton from the proton participating in the hard scattering is extracted. Then, the underlying event is generated simulating a pPb collision through HIJING, but with the energy of the proton decreased according to the value extracted in the previous step, and both collisions are superimposed. This model is able to capture the bulk of the centrality effect for central to semicentral collisions, for the two available sets of data: dijets from the CMS Collaboration and...

  5. 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 qualitatively with invitation in both groups. In Group A, out-of-hours contact with family was statistically associated with invitation (adjusted odds ratio 5.46 (95% confidence interval 2.13-14.00); p Qualitative findings also indicated that clinicians' perceptions of families' wellbeing, circumstances, characteristics, engagement with clinicians and anticipated reaction to invitation influenced invitation. We found evidence of selective invitation practices that could bias research findings. Non-invitation and selection bias should be considered, assessed and reported in palliative care studies. © The Author(s) 2014.

  6. 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.

  7. Macro-spin modeling and experimental study of spin-orbit torque biased magnetic sensors

    Science.gov (United States)

    Xu, Yanjun; Yang, Yumeng; Luo, Ziyan; Xu, Baoxi; Wu, Yihong

    2017-11-01

    We reported a systematic study of spin-orbit torque biased magnetic sensors based on NiFe/Pt bilayers through both macro-spin modeling and experiments. The simulation results show that it is possible to achieve a linear sensor with a dynamic range of 0.1-10 Oe, power consumption of 1 μW-1mW, and sensitivity of 0.1-0.5 Ω/Oe. These characteristics can be controlled by varying the sensor dimension and current density in the Pt layer. The latter is in the range of 1 × 105-107 A/cm2. Experimental results of fabricated sensors with selected sizes agree well with the simulation results. For a Wheatstone bridge sensor comprising of four sensing elements, a sensitivity up to 0.548 Ω/Oe, linearity error below 6%, and detectivity of about 2.8 nT/√Hz were obtained. The simple structure and ultrathin thickness greatly facilitate the integration of these sensors for on-chip applications. As a proof-of-concept experiment, we demonstrate its application in detection of current flowing in an on-chip Cu wire.

  8. 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...

  9. Measurement Error and Bias in Value-Added Models. Research Report. ETS RR-17-25

    Science.gov (United States)

    Kane, Michael T.

    2017-01-01

    By aggregating residual gain scores (the differences between each student's current score and a predicted score based on prior performance) for a school or a teacher, value-added models (VAMs) can be used to generate estimates of school or teacher effects. It is known that random errors in the prior scores will introduce bias into predictions of…

  10. Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media

    Science.gov (United States)

    Cooley, R.L.; Christensen, S.

    2006-01-01

    Groundwater models need to account for detailed but generally unknown spatial variability (heterogeneity) of the hydrogeologic model inputs. To address this problem we replace the large, m-dimensional stochastic vector ?? that reflects both small and large scales of heterogeneity in the inputs by a lumped or smoothed m-dimensional approximation ????*, where ?? is an interpolation matrix and ??* is a stochastic vector of parameters. Vector ??* has small enough dimension to allow its estimation with the available data. The consequence of the replacement is that model function f(????*) written in terms of the approximate inputs is in error with respect to the same model function written in terms of ??, ??,f(??), which is assumed to be nearly exact. The difference f(??) - f(????*), termed model error, is spatially correlated, generates prediction biases, and causes standard confidence and prediction intervals to be too small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate ??* 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 regression methods are extended to analyze the revised method. The analysis develops analytical expressions for bias terms reflecting the interaction of model nonlinearity and model error, for correction factors needed to adjust the sizes of confidence and prediction intervals for this interaction, and for correction factors needed to adjust the sizes of confidence and prediction intervals for possible use of a diagonal weight matrix in place of the correct one. If terms expressing the degree of intrinsic nonlinearity for f(??) and f(????*) are small, then most of the biases are small and the correction factors are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is

  11. Age Bias in Selection Decisions: The Role of Facial Appearance and Fitness Impressions

    Directory of Open Access Journals (Sweden)

    Michèle C. Kaufmann

    2017-12-01

    Full Text Available This research examined the impact of facial age appearance on hiring, and impressions of fitness as the underlying mechanism. In two experimental hiring simulations, one with lay persons and one with Human Resource professionals, participants evaluated a chronologically older or younger candidate (as indicated by date of birth and age label with either younger or older facial age appearance (as indicated by a photograph. In both studies, older-looking candidates received lower hireability ratings, due to less favorable fitness impressions. In addition, Study 1 showed that this age bias was reduced when the candidates provided counter-stereotypic information about their fitness. Study 2 showed that facial age-based discrimination is less prevalent in jobs with less costumer contact (e.g., back office.

  12. Partisan Bias in Message Selection: Media Gatekeeping of Party Press Releases.

    Science.gov (United States)

    Haselmayer, Martin; Wagner, Markus; Meyer, Thomas M

    2017-07-03

    Parties try to shape media coverage in ways that are favorable to them, but what determines whether media outlets pick up and report on party messages? Based on content analyses of 1,496 party press releases and 6,512 media reports from the 2013 Austrian parliamentary election campaign, we show that media coverage of individual party messages is influenced not just by news factors, but also by partisan bias. The media are therefore more likely to report on messages from parties their readers favor. Importantly, this effect is greater rather than weaker when these messages have high news value. These findings have important implications for understanding the media's role in elections and representative democracies in general.

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

    Science.gov (United States)

    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

  14. Simple full micromagnetic model of exchange bias behavior in ferro/antiferromagnetic layered structures (abstract)

    Science.gov (United States)

    Koon, Norman C.

    1997-04-01

    It is shown using full micromagnetic relaxation calculations that exchange bias behavior is predicted for single-crystal ferro/antiferromagnetic layers with a fully compensated interface. The particular example most fully studied has a bcc/bct lattice structure with a fully compensated (110) interface plane. Only bilinear Heisenberg exchange was assumed, with anisotropy only in the antiferromagnet. In spite of the intuitive notion that exchange coupling between a ferromagnet and an antiferromagnet across a fully compensated plane of the antiferromagnet should be zero, we find strong coupling, comparable to the bilinear exchange, with a 90° angle between the ferromagnetic and antiferromagnetic axes of layers far from the interface in absence of an applied field. Even though the 90° coupling has characteristics resembling "biquadratic" exchange, it originates entirely from frustrated bilinear exchange. The development of exchange bias is found to originate from the formation of a domain wall in the antiferromagnet via the strong 90° exchange coupling and pinning of the wall by the magnetocrystalline anisotropy in the antiferromagnet. Because the large demagnetizing factor of the ferromagnet tends to confine its magnetization to the plane, the exchange bias is found to depend mainly on the strength and the symmetry of the in-plane component of anisotropy. Although little effort was made to analyze specific systems, the model reproduces many of the qualitative features observed in real exchange bias systems and gives reasonable semiquantitative estimates for the bias field when exchange and anisotropy values consistent with real systems are used.

  15. Model selection for univariable fractional polynomials.

    Science.gov (United States)

    Royston, Patrick

    2017-07-01

    Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429-467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.

  16. Measuring Teaching Quality in Higher Education : Assessing the Problem of Selection Bias in Course Evaluations

    OpenAIRE

    Salomons, Anna; Goos, Maarten

    2014-01-01

    Student evaluations of teaching are widely used to measure teaching quality and compare it across different courses, teachers, departments and institutions: as such, they are of increasing importance for teacher promotion decisions as well as student course selection. However, the response on course evaluations is rarely perfect, rendering such uses unwarranted if students who participate in the evaluation are not randomly selected: this paper is the first to investigate this issue. We quanti...

  17. Profile of rheumatology patients willing to report adverse drug reactions: bias from selective reporting

    Directory of Open Access Journals (Sweden)

    Protić D

    2016-02-01

    Full Text Available Dragana Protić,1 Nada Vujasinović-Stupar,2 Zoran Bukumirić,3 Slavica Pavlov-Dolijanović,4 Snežana Baltić,5 Slavica Mutavdžin,6 Ljiljana Markovic-Denić,7 Marija Zdravković,8 Zoran Todorović1 1Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 2Department 2, Institute of Rheumatology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 3Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 4Department 5, Institute of Rheumatology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 5Department 5, Institute of Rheumatology, Belgrade, Serbia; 6Institute of Physiology “Rihard Burjan”, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 7Institute of Epidemiology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 8Department of Cardiology, Medical Center “Bežanijska kosa”, Faculty of Medicine, University of Belgrade, Belgrade, Serbia Background: Adverse drug reactions (ADRs have a significant impact on human health and health care costs. The aims of our study were to determine the profile of rheumatology patients willing to report ADRs and to identify bias in such a reporting system. Methods: Semi-intensive ADRs reporting system was used in our study. Patients willing to participate (N=261 completed the questionnaire designed for the purpose of the study at the hospital admission. They were subsequently classified into two groups according to their ability to identify whether they had experienced ADRs during the previous month. Group 1 included 214 out of 261 patients who were able to identify ADRs, and group 2 consisted of 43 out of 261 patients who were not able to identify ADRs in their recent medical history. Results: Group 1 patients were more significantly aware of their diagnosis than the patients from group 2. Marginal significance was found

  18. 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

  19. 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.

  20. Minimum Bias Trigger in ATLAS

    CERN Document Server

    Kwee, R E; The ATLAS collaboration

    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 2.09 < |eta| < 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 presen...

  1. Land surface albedo bias in climate models and its association with tropical rainfall

    Science.gov (United States)

    Levine, Xavier J.; Boos, William R.

    2017-06-01

    The influence of surface albedo on tropical precipitation is widely appreciated, but albedo bias over snow-free areas in climate models has been studied little. Here historical Coupled Model Intercomparison Project Phase 5 simulations are shown to exhibit large multimodel mean bias and intermodel variability in boreal summer mean surface broadband shortwave albedo. Intermodel variability in this albedo is globally coherent over vegetated regions and correlates with intermodel tropical precipitation variability. Evidence supports the hypothesis that these spatially coherent albedo variations cause precipitation variations. Specifically, spatial structures of albedo and precipitation variations are distinct, suggesting the latter do not cause the former by darkening soil. Furthermore, simulated interannual albedo variance is small compared to intermodel albedo variance, while the ratio of interannual to intermodel precipitation variance is much larger. Finally, imposing the dominant pattern of intermodel albedo variability in one climate model causes a precipitation change with structure similar to that of the intermodel variability.

  2. 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.

  3. Measuring Teaching Quality in Higher Education: Assessing Selection Bias in Course Evaluations

    Science.gov (United States)

    Goos, Maarten; Salomons, Anna

    2017-01-01

    Student evaluations of teaching (SETs) are widely used to measure teaching quality in higher education and compare it across different courses, teachers, departments and institutions. Indeed, SETs are of increasing importance for teacher promotion decisions, student course selection, as well as for auditing practices demonstrating institutional…

  4. Measuring Teaching Quality in Higher Education : Assessing the Problem of Selection Bias in Course Evaluations

    NARCIS (Netherlands)

    Salomons, Anna; Goos, Maarten

    2014-01-01

    Student evaluations of teaching are widely used to measure teaching quality and compare it across different courses, teachers, departments and institutions: as such, they are of increasing importance for teacher promotion decisions as well as student course selection. However, the response on course

  5. 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.

  6. 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 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.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  9. Validity of tests under covariate-adaptive biased coin randomization and generalized linear models.

    Science.gov (United States)

    Shao, Jun; Yu, Xinxin

    2013-12-01

    Some covariate-adaptive randomization methods have been used in clinical trials for a long time, but little theoretical work has been done about testing hypotheses under covariate-adaptive randomization until Shao et al. (2010) who provided a theory with detailed discussion for responses under linear models. In this article, we establish some asymptotic results for covariate-adaptive biased coin randomization under generalized linear models with possibly unknown link functions. We show that the simple t-test without using any covariate is conservative under covariate-adaptive biased coin randomization in terms of its Type I error rate, and that a valid test using the bootstrap can be constructed. This bootstrap test, utilizing covariates in the randomization scheme, is shown to be asymptotically as efficient as Wald's test correctly using covariates in the analysis. Thus, the efficiency loss due to not using covariates in the analysis can be recovered by utilizing covariates in covariate-adaptive biased coin randomization. Our theory is illustrated with two most popular types of discrete outcomes, binary responses and event counts under the Poisson model, and exponentially distributed continuous responses. We also show that an alternative simple test without using any covariate under the Poisson model has an inflated Type I error rate under simple randomization, but is valid under covariate-adaptive biased coin randomization. Effects on the validity of tests due to model misspecification is also discussed. Simulation studies about the Type I errors and powers of several tests are presented for both discrete and continuous responses. © 2013, The International Biometric Society.

  10. The Application of Model Life Table Systems in China: Assessment of System Bias and Error

    Directory of Open Access Journals (Sweden)

    Songbo Hu

    2014-12-01

    Full Text Available and projection. Although China is the world's most populous country with approximately a fifth of the world's population, none of the empirical tables from mainland China were used in calibrating the existing models. In this paper, we applied recent three model life table systems with different inputs to China mortality data to investigate whether or not these systems truly reflect Chinese mortality epidemiological patterns and whether or not system biases exist. The resulting residuals show that, in most cases, the male infant mortality rate (1q0, adult mortality rate (45q15 and old age mortality rate (20q60 have a strong bias towards being overestimated and the life expectancy at birth (e0 bias is underestimated. We also give the detailed results for each case. Furthermore, we found that the average relative errors (AREs for females are more than those for males for e0, 45q15 and 20q60, but for 1q0, males have larger AREs in the Wilmoth and Murray systems. We also found that the urban population has more errors than the rural population in almost all cases. Finally, by comparing the AREs with 10 other countries, we found the errors for China are more than those for other countries in most cases. It is concluded that these existing model life table systems cannot accurately reflect Chinese mortality epidemiological situations and trajectories. Therefore, model life tables should be used with caution when applied to China on the basis of 5q0.

  11. The application of model life table systems in China: assessment of system bias and error.

    Science.gov (United States)

    Hu, Songbo; Yu, Chuanhua

    2014-12-01

    and projection. Although China is the world's most populous country with approximately a fifth of the world's population, none of the empirical tables from mainland China were used in calibrating the existing models. In this paper, we applied recent three model life table systems with different inputs to China mortality data to investigate whether or not these systems truly reflect Chinese mortality epidemiological patterns and whether or not system biases exist. The resulting residuals show that, in most cases, the male infant mortality rate (1q0), adult mortality rate (45q15) and old age mortality rate (20q60) have a strong bias towards being overestimated and the life expectancy at birth (e0) bias is underestimated. We also give the detailed results for each case. Furthermore, we found that the average relative errors (AREs) for females are more than those for males for e0, 45q15 and 20q60, but for 1q0, males have larger AREs in the Wilmoth and Murray systems. We also found that the urban population has more errors than the rural population in almost all cases. Finally, by comparing the AREs with 10 other countries, we found the errors for China are more than those for other countries in most cases. It is concluded that these existing model life table systems cannot accurately reflect Chinese mortality epidemiological situations and trajectories. Therefore, model life tables should be used with caution when applied to China on the basis of 5q0.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

    OpenAIRE

    Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang

    2015-01-01

    The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...

  17. A right-ear bias of auditory selective attention is evident in alpha oscillations.

    Science.gov (United States)

    Payne, Lisa; Rogers, Chad S; Wingfield, Arthur; Sekuler, Robert

    2017-04-01

    Auditory selective attention makes it possible to pick out one speech stream that is embedded in a multispeaker environment. We adapted a cued dichotic listening task to examine suppression of a speech stream lateralized to the nonattended ear, and to evaluate the effects of attention on the right ear's well-known advantage in the perception of linguistic stimuli. After being cued to attend to input from either their left or right ear, participants heard two different four-word streams presented simultaneously to the separate ears. Following each dichotic presentation, participants judged whether a spoken probe word had been in the attended ear's stream. We used EEG signals to track participants' spatial lateralization of auditory attention, which is marked by interhemispheric differences in EEG alpha (8-14 Hz) power. A right-ear advantage (REA) was evident in faster response times and greater sensitivity in distinguishing attended from unattended words. Consistent with the REA, we found strongest parietal and right frontotemporal alpha modulation during the attend-right condition. These findings provide evidence for a link between selective attention and the REA during directed dichotic listening. © 2016 Society for Psychophysiological Research.

  18. 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.

  19. 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.

  20. Selection bias: Examining the feasibility, utility, and participant receptivity to incorporating simulation into the general surgery residency selection process.

    Science.gov (United States)

    Gardner, Aimee K; Steffes, Christopher P; Nepomnayshy, Dmitry; Nicholas, Cate; Widmann, Warren D; Fitzgibbons, Shimae C; Dunkin, Brian J; Jones, Daniel B; Paige, John T

    2017-06-01

    Opportunities exist to revise the current residency selection process to capture desirable candidate competencies. We examined the extent to which components of the American College of Surgeons/Association for Surgical Education simulation-based medical student curriculum combined with a teamwork activity could be used as potential screening method. Students participated in a workshop consisting of training/evaluation of knot tying, suturing, airway management, gowning/gloving, and teamwork. Surveys were given to medical students (MS) and faculty/resident/staff (FRS) to examine their opinions about the residency screening process, the most critical competencies to assess, and the effectiveness of each station for candidate evaluation. Communication (FRS, 4.86 ± .35; MS, 4.93 ± .26), leadership (FRS, 4.41 ± .80; MS, 4.5 ± .76), judgment (FRS, 4.62 ± .74; MS, 4.67 ± .62), professionalism (FRS, 4.64 ± .73; MS, 5.00 ± .00), integrity (FRS, 4.71 ± .78; MS, 4.87 ± .35), and grit/resilience (FRS, 4.71 ± .78; MS, 4.53 ± .74) were considered most valuable for candidate screening. The simulation-based curriculum for evaluation of residency candidates was rated lowest by both groups. Open response comments indicated positive perceptions of this process. Employing simulation to assess candidates may be most beneficial for examining nontechnical attributes. Future work should continue to explore this area. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Biased small-molecule ligands for selective inhibition of HIV-1 cell entry via CCR5

    DEFF Research Database (Denmark)

    Berg, Christian; Spiess, Katja; von Lüttichau, Hans Rudolf

    2016-01-01

    Since the discovery of HIV's use of CCR5 as the primary coreceptor in fusion, the focus on developing small-molecule receptor antagonists for inhibition hereof has only resulted in one single drug, Maraviroc. We therefore investigated the possibility of using small-molecule CCR5 agonists as HIV-1...... fusion inhibitors. A virus-free cell-based fusion reporter assay, based on mixing "effector cells" (expressing HIV Env and luciferase activator) with "target cells" (expressing CD4, CCR5 wild type or a selection of well-described mutations, and luciferase reporter), was used as fusion readout. Receptor...... expression was evaluated by ELISA and fluorescence microscopy. On CCR5 WT, Maraviroc and Aplaviroc inhibited fusion with high potencies (EC 50 values of 91 and 501 nM, respectively), whereas removal of key residues for both antagonists (Glu283Ala) or Maraviroc alone (Tyr251Ala) prevented fusion inhibition...

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

    Science.gov (United States)

    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

  3. Bias Correction for climate impact modeling within the framework of the HAPPI Initiative

    Science.gov (United States)

    Saeed, Fahad; Lange, Stefan; Schleussner, Carl-Friedrich

    2017-04-01

    In its landmark Paris Agreement of 2015, the Conference of the Parties of the United Nations Framework Convention on Climate Change (UNFCCC) invited the IPCC to prepare a special report "on the impacts of global warming of 1.5°C above pre-industrial levels and related greenhouse gas emission pathways" by 2018. Unfortunately, most current experiments (including Coupled Model Inter-comparison Project (CMIP)), are not specifically designed for making a substantial contribution to this report. To fill this gap, the HAPPI (Half a degree Additional warming, Projection, Prognosis and Impacts) initiative has been designed to assess climate projections, and in particular extreme weather, at present day and in worlds that are 1.5°C and 2.0°C warmer than pre-industrial conditions? Global Climate Model (GCM) output for HAPPI will be utilized to assess climate impacts with a range of sectorial climate impact models. Before the use of climate data as input for sectorial impact models, statistical bias correction is commonly applied to correct climate model data for systematic deviations of the simulated historic data from observations and to increase the accuracy of the projections. Different approaches have been adopted for this purpose, however the most common are the one based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. In the current study, we presented results for a novel bias correction method developed for Inter-Sectoral Impact Model Intercomparison Project Phase 2b (ISIMIP2b) applied to output of different GCMs generated within the HAPPI project. The results indicate that the application of bias correction has shown substantial improvement in the results when we compared to observational data. Besides the marked improvement in seasonal mean differences for different variables, also the output for extreme event indicators is considerably improved. We conclude that the applied application of bias

  4. Precipitation by a regional climate model and bias correction in Europe and South Asia

    Energy Technology Data Exchange (ETDEWEB)

    Dobler, A.; Ahrens, B. [Inst. for Atmospheric and Environmental Sciences, Goethe-Univ., Frankfurt am Main (Germany)

    2008-08-15

    Because coarse-grid global circulation models do not allow for regional estimates of the water balance or trends of extreme precipitation, downscaling of global simulations is necessary to generate regional precipitation. This paper applies for downscaling the regional climate model CLM as a dynamical downscaling method (DDM) and two statistical downscaling methods (SDMs). Because the SDMs neglect information available to the DDM, and vice versa, a combination of the dynamical and statistical approaches is proposed here. In this combined approach, a simple statistical step is carried out to correct for the regional model biases in the dynamically downscaled simulations. To test the proposed methods, coarse-grid global re-analysis data (ERA40 with {proportional_to}1.125 grid spacing) is downscaled in two regions with different climatology and orography: one in South Asia and the other in Europe. All of the methods are tested on daily precipitation with 0.5 grid spacing. The SDMs are generally successful: the standardized root mean square error of rain day intensity is reduced from ERA40's 0.16 to 0.10 in a test area to the west of the European Alps. The CLM simulations perform less well (with a corresponding error of 0.14), but represent a promising approach if the user requires flexibility and independence from observational data. The proposed bias correction of the CLM simulations performs very well in European test areas (better than or at least comparable with the SDMs; i.e., with a corresponding error of 0.07), but fails in South Asia. An investigation of the observed and simulated precipitation climate in the test areas shows a strong dependence of the bias correction performance on sampling statistics (i.e., rain day frequency) and on the robustness of bias estimation. (orig.)

  5. Selected sports talent development models

    OpenAIRE

    Michal Vičar

    2017-01-01

    Background: Sports talent in the Czech Republic is generally viewed as a static, stable phenomena. It stands in contrast with widespread praxis carried out in Anglo-Saxon countries that emphasise its fluctuant nature. This is reflected in the current models describing its development. Objectives: The aim is to introduce current models of talent development in sport. Methods: Comparison and analysing of the following models: Balyi - Long term athlete development model, Côté - Developmen...

  6. Application of adaptive kinetic modelling for bias propagation reduction in direct 4D image reconstruction

    International Nuclear Information System (INIS)

    Kotasidis, F A; Zaidi, H; Matthews, J C; Reader, A J; Angelis, G I

    2014-01-01

    Parametric imaging in thoracic and abdominal PET can provide additional parameters more relevant to the pathophysiology of the system under study. However, dynamic data in the body are noisy due to the limiting counting statistics leading to suboptimal kinetic parameter estimates. Direct 4D image reconstruction algorithms can potentially improve kinetic parameter precision and accuracy in dynamic PET body imaging. However, construction of a common kinetic model is not always feasible and in contrast to post-reconstruction kinetic analysis, errors in poorly modelled regions may spatially propagate to regions which are well modelled. To reduce error propagation from erroneous model fits, we implement and evaluate a new approach to direct parameter estimation by incorporating a recently proposed kinetic modelling strategy within a direct 4D image reconstruction framework. The algorithm uses a secondary more general model to allow a less constrained model fit in regions where the kinetic model does not accurately describe the underlying kinetics. A portion of the residuals then is adaptively included back into the image whilst preserving the primary model characteristics in other well modelled regions using a penalty term that trades off the models. Using fully 4D simulations based on dynamic [ 15 O]H 2 O datasets, we demonstrate reduction in propagation-related bias for all kinetic parameters. Under noisy conditions, reductions in bias due to propagation are obtained at the cost of increased noise, which in turn results in increased bias and variance of the kinetic parameters. This trade-off reflects the challenge of separating the residuals arising from poor kinetic modelling fits from the residuals arising purely from noise. Nonetheless, the overall root mean square error is reduced in most regions and parameters. Using the adaptive 4D image reconstruction improved model fits can be obtained in poorly modelled regions, leading to reduced errors potentially

  7. Application of adaptive kinetic modelling for bias propagation reduction in direct 4D image reconstruction.

    Science.gov (United States)

    Kotasidis, F A; Matthews, J C; Reader, A J; Angelis, G I; Zaidi, H

    2014-10-21

    Parametric imaging in thoracic and abdominal PET can provide additional parameters more relevant to the pathophysiology of the system under study. However, dynamic data in the body are noisy due to the limiting counting statistics leading to suboptimal kinetic parameter estimates. Direct 4D image reconstruction algorithms can potentially improve kinetic parameter precision and accuracy in dynamic PET body imaging. However, construction of a common kinetic model is not always feasible and in contrast to post-reconstruction kinetic analysis, errors in poorly modelled regions may spatially propagate to regions which are well modelled. To reduce error propagation from erroneous model fits, we implement and evaluate a new approach to direct parameter estimation by incorporating a recently proposed kinetic modelling strategy within a direct 4D image reconstruction framework. The algorithm uses a secondary more general model to allow a less constrained model fit in regions where the kinetic model does not accurately describe the underlying kinetics. A portion of the residuals then is adaptively included back into the image whilst preserving the primary model characteristics in other well modelled regions using a penalty term that trades off the models. Using fully 4D simulations based on dynamic [(15)O]H2O datasets, we demonstrate reduction in propagation-related bias for all kinetic parameters. Under noisy conditions, reductions in bias due to propagation are obtained at the cost of increased noise, which in turn results in increased bias and variance of the kinetic parameters. This trade-off reflects the challenge of separating the residuals arising from poor kinetic modelling fits from the residuals arising purely from noise. Nonetheless, the overall root mean square error is reduced in most regions and parameters. Using the adaptive 4D image reconstruction improved model fits can be obtained in poorly modelled regions, leading to reduced errors potentially propagating

  8. Exploring the common molecular basis for the universal DNA mutation bias: revival of Löwdin mutation model.

    Science.gov (United States)

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

    2011-06-10

    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 Löwdin 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, Löwdin model offers a common explanation for the observed universal mutation bias and thus has broad biological implications. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. 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.

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

    Science.gov (United States)

    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.

  11. MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS

    International Nuclear Information System (INIS)

    Asensio Ramos, A.; Manso Sainz, R.; Martínez González, M. J.; Socas-Navarro, H.; Viticchié, B.; Orozco Suárez, D.

    2012-01-01

    Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.

  12. 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.

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Summer N. Clay

    2017-10-01

    Full Text Available 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.

  15. Causes and Implications of Persistent Atmospheric Carbon Dioxide Biases in Earth System Models

    Energy Technology Data Exchange (ETDEWEB)

    Hoffman, Forrest M [ORNL; Randerson, James T. [University of California, Irvine; Arora, Vivek K. [Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada; Bao, Qing [State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics; Cadule, Patricia [Institut Pierre Simon Laplace, Laboratoire des Sciences du Climat et de l' Environment; Ji, Duoying [State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing; Jones, Chris D. [Hadley Centre, U.K. Met Office; Kawamiya, Michio [Japan Agency for Marine-Earth Science and Technology (JAMSTEC); Khatiwala, Samar [Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY; Lindsay, Keith [National Center for Atmospheric Research (NCAR); Obata, Atsushi [Meteorological Research Institute, Japan; Shevliakova, Elena [Princeton University; Six, Katharina D. [Max Planck Institute for Meteorology, Hamburg, Germany; Tjiputra, Jerry F. [Uni Climate, Uni Research; Volodin, Evgeny M. [Institute of Numerical Mathematics, Russian Academy of Science, Moscow; Wu, Tongwen [China Meteorological Administration (CMA), Beijing

    2014-01-01

    The strength of feedbacks between a changing climate and future CO2 concentrations are uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations--in which atmospheric CO2 levels were computed prognostically--for historical (1850-2005) and future periods (RCP 8.5 for 2006-2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2 levels for the multi-model ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2 estimates of 600 {plus minus} 14 ppm at 2060 and 947 {plus minus} 35 ppm at 2100, which were 21 ppm and 32 ppm below the multi-model mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2-induced radiative forcing, and CO2-induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era, and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO2 from Mauna Loa, our analysis suggests uncertainties in

  16. VEMAP 1: Selected Model Results

    Data.gov (United States)

    National Aeronautics and Space Administration — The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and...

  17. Assessment of Ionospheric Spatial Decorrelation for CAT I GBAS in Equatorial Region at Nominal days: Data Selection and Bias Removal

    Science.gov (United States)

    Chang, H.; Lee, J.

    2017-12-01

    Ground-based augmentations of global positioning system (GBAS) provide the user with the integrity parameter, standard deviation of vertical ionospheric gradient (σvig), to ensure integrity. σvig value currently available in CAT I GBAS is derived from the data collected from the reference stations located on the US mainland and have a value of 4 mm/km. However, since the equatorial region near the geomagnetic equator is relatively more active in the ionosphere than the mid-latitude region, there is a limit to applying σvig used in the mid-latitude region on the equatorial region. Also, since the ionospheric phenomena of daytime and nighttime in the equatorial region are significantly different, it is necessary to apply σvig whilst distinguishing the time zone. This study presents a method for obtaining standard deviation of vertical ionospheric gradient in the equatorial region at nominal days considering the equatorial ionosphere environment. We used the data collected from the Brazilian region near the geomagnetic equator in the nominal days. One of the distinguishing features of the equatorial ionosphere environment from the mid-latitude ionosphere environment is that the scintillation event occurs frequently. Therefore, the days used for the analysis were selected not only by geomagnetic indexes Kp (Planetary K index) and Dst (Disturbance storm index), but also by S4 (Scintillation index) which indicates scintillation event. In addition, unlike the ionospheric delay bias elimination method used in the mid-latitude region, the `Long-term ionospheric anomaly monitor (LTIAM)' used in this study utilized the bias removal method that applies different bias removal standards according to IPP (Ionospheric pierce point) distance in consideration of ionospheric activity. As a result, σvig values which are conservative enough to bound ionosphere spatial decorrelation for the equatorial region in nominal days are 8 mm/km for daytime and 19 mm/km for nighttime

  18. Fine-tuning translation kinetics selection as the driving force of codon usage bias in the hepatitis A virus capsid.

    Directory of Open Access Journals (Sweden)

    Lluís Aragonès

    2010-03-01

    Full Text Available Hepatitis A virus (HAV, the prototype of genus Hepatovirus, has several unique biological characteristics that distinguish it from other members of the Picornaviridae family. Among these, the need for an intact eIF4G factor for the initiation of translation results in an inability to shut down host protein synthesis by a mechanism similar to that of other picornaviruses. Consequently, HAV must inefficiently compete for the cellular translational machinery and this may explain its poor growth in cell culture. In this context of virus/cell competition, HAV has strategically adopted a naturally highly deoptimized codon usage with respect to that of its cellular host. With the aim to optimize its codon usage the virus was adapted to propagate in cells with impaired protein synthesis, in order to make tRNA pools more available for the virus. A significant loss of fitness was the immediate response to the adaptation process that was, however, later on recovered and more associated to a re-deoptimization rather than to an optimization of the codon usage specifically in the capsid coding region. These results exclude translation selection and instead suggest fine-tuning translation kinetics selection as the underlying mechanism of the codon usage bias in this specific genome region. Additionally, the results provide clear evidence of the Red Queen dynamics of evolution since the virus has very much evolved to re-adapt its codon usage to the environmental cellular changing conditions in order to recover the original fitness.

  19. The linear utility model for optimal selection

    NARCIS (Netherlands)

    Mellenbergh, Gideon J.; van der Linden, Willem J.

    A linear utility model is introduced for optimal selection when several subpopulations of applicants are to be distinguished. Using this model, procedures are described for obtaining optimal cutting scores in subpopulations in quota-free as well as quota-restricted selection situations. The cutting

  20. VEMAP 1: Selected Model Results

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and...

  1. Exploring Several Methods of Groundwater Model Selection

    Science.gov (United States)

    Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar

    2017-04-01

    Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).

  2. 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

    abundance relative to the conventional plug-in estimator, and also gives essentially identical estimates to a sample-based bias-correction estimator. The epsilon-method has been implemented by us as a generic option in the open-source Template Model Builder software, and could be adapted within other...... is calculated as a nonlinear function of random effects. We therefore describe and evaluate a new "epsilon" estimator as a generic bias-correction estimator for derived quantities. We use simulated data to compare the epsilon-method with an existing bias-correction algorithm for estimating recruitment in four...... 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...

  3. Self-objectification, weight bias internalization, and binge eating in young women: Testing a mediational model.

    Science.gov (United States)

    Mehak, Adrienne; Friedman, Aliza; Cassin, Stephanie E

    2018-01-18

    Self-objectification and weight bias internalization are two internalization processes that are positively correlated with binge eating among young women. However, the mechanisms underlying these relationships are understudied. Consistent with objectification theory, this study examined appearance anxiety and body shame as mediators between self-objectification, weight bias internalization and binge eating. Female undergraduates (N = 102) completed self-report measures of self-objectification, weight bias internalization, appearance anxiety, body shame, and binge eating. Results indicated that women who self-objectified and internalized negative weight-related attitudes reported greater binge eating (r s  = .43 and r s  = .57, respectively) and these associations were mediated by the combined effects of body shame and appearance anxiety. The contrast between the two mediators was also significant, such that body shame emerged as a stronger mediator within both mediational models. Results demonstrated that these internalization processes contribute to negative affect in young women, which may in turn lead to binge eating. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. 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.

  5. Impact of bias discrepancy and amino acid usage on estimates of the effective number of codons used in a gene, and a test for selection on codon usage

    DEFF Research Database (Denmark)

    Fuglsang, Anders

    2007-01-01

    The effective number of codons (Nc) used in a gene is one of the most commonly used measures of synonymous codon usage bias, owing much of its popularity to the fact that it is species independent and that simulation studies have shown that it is less dependent of gene length than other measures....... In this paper I provide a clear and practically meaningful definition of bias discrepancy (BD; when the degree of codon bias varies within a degeneracy class). Moreover I evaluate the impact of BD and amino acid usage on estimates of Nc. It is shown that both factors have a significant effect on accuracy...... and precision. Both amino acid usage and BD influence accuracy considerably, especially in short genes. Finally, I demonstrate how the definition of bias discrepancy can be applied to investigate if codon usage is influenced by selection and I discuss this test in relation to the incongruous literature...

  6. Selection of classification models from repository of model for water ...

    African Journals Online (AJOL)

    This paper proposes a new technique, Model Selection Technique (MST) for selection and ranking of models from the repository of models by combining three performance measures (Acc, TPR and TNR). This technique provides weightage to each performance measure to find the most suitable model from the repository of ...

  7. 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.

  8. Exchange bias and asymmetric hysteresis loops from a microscopic model of core/shell nanoparticles

    International Nuclear Information System (INIS)

    Iglesias, Oscar; Batlle, Xavier; Labarta, Amilcar

    2007-01-01

    We present Monte Carlo simulations of hysteresis loops of a model of a magnetic nanoparticle with a ferromagnetic core and an antiferromagnetic shell with varying values of the core/shell interface exchange coupling which aim to clarify the microscopic origin of exchange bias observed experimentally. We have found loop shifts in the field direction as well as displacements along the magnetization axis that increase in magnitude when increasing the interfacial exchange coupling. Overlap functions computed from the spin configurations along the loops have been obtained to explain the origin and magnitude of these features microscopically

  9. Alternatives to accuracy and bias metrics based on percentage errors for radiation belt modeling applications

    Energy Technology Data Exchange (ETDEWEB)

    Morley, Steven Karl [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-07-01

    This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.

  10. A Multimodel Study on Warm Precipitation Biases in Global Models Compared to Satellite Observations

    Science.gov (United States)

    Jing, Xianwen; Suzuki, Kentaroh; Guo, Huan; Goto, Daisuke; Ogura, Tomoo; Koshiro, Tsuyoshi; Mülmenstädt, Johannes

    2017-11-01

    The cloud-to-precipitation transition process in warm clouds simulated by state-of-the-art global climate models (GCMs), including both traditional climate models and a high-resolution model, is evaluated against A-Train satellite observations. The models and satellite observations are compared in the form of the statistics obtained from combined analysis of multiple-satellite observables that probe signatures of the cloud-to-precipitation transition process. One common problem identified among these models is the too-frequent occurrence of warm precipitation. The precipitation is found to form when the cloud particle size and the liquid water path (LWP) are both much smaller than those in observations. The too-efficient formation of precipitation is found to be compensated for by errors of cloud microphysical properties, such as underestimated cloud particle size and LWP, to an extent that varies among the models. However, this does not completely cancel the precipitation formation bias. Robust errors are also found in the evolution of cloud microphysical properties from nonprecipitating to drizzling and then to raining clouds in some GCMs, implying unrealistic interaction between precipitation and cloud water. Nevertheless, auspicious information is found for future improvement of warm precipitation representations: the adoption of more realistic autoconversion scheme in the high-resolution model improves the triggering of precipitation, and the introduction of a sophisticated subgrid variability scheme in a traditional model improves the simulated precipitation frequency over subtropical eastern ocean. However, deterioration in other warm precipitation characteristics is also found accompanying these improvements, implying the multisource nature of warm precipitation biases in GCMs.

  11. Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring.

    Science.gov (United States)

    Radestock, Sebastian; Weil, Tanja; Renner, Steffen

    2008-05-01

    The current study investigates the combination of two recently reported techniques for the improvement of homology model-based virtual screening for G-protein coupled receptor (GPCR) ligands. First, ligand-supported homology modeling was used to generate receptor models that were in agreement with mutagenesis data and structure-activity relationship information of the ligands. Second, interaction patterns from known ligands to the receptor were applied for scoring and rank ordering compounds from a virtual library using ligand-receptor interaction fingerprint-based similarity (IFS). Our approach was evaluated in retrospective virtual screening experiments for antagonists of the metabotropic glutamate receptor (mGluR) subtype 5. The results of our approach were compared to the results obtained by conventional scoring functions (Dock-Score, PMF-Score, Gold-Score, ChemScore, and FlexX-Score). The IFS lead to significantly higher enrichment rates, relative to the competing scoring functions. Though using a target-biased scoring approach, the results were not biased toward the chemical classes of the reference structures. Our results indicate that the presented approach has the potential to serve as a general setup for successful structure-based GPCR virtual screening.

  12. Biased random key genetic algorithm with insertion and gender selection for capacitated vehicle routing problem with time windows

    Science.gov (United States)

    Rochman, Auliya Noor; Prasetyo, Hari; Nugroho, Munajat Tri

    2017-06-01

    Vehicle Routing Problem (VRP) often occurs when the manufacturers need to distribute their product to some customers/outlets. The distribution process is typically restricted by the capacity of the vehicle and the working hours at the distributor. This type of VRP is also known as Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A Biased Random Key Genetic Algorithm (BRKGA) was designed and coded in MATLAB to solve the CVRPTW case of soft drink distribution. The standard BRKGA was then modified by applying chromosome insertion into the initial population and defining chromosome gender for parent undergoing crossover operation. The performance of the established algorithms was then compared to a heuristic procedure for solving a soft drink distribution. Some findings are revealed (1) the total distribution cost of BRKGA with insertion (BRKGA-I) results in a cost saving of 39% compared to the total cost of heuristic method, (2) BRKGA with the gender selection (BRKGA-GS) could further improve the performance of the heuristic method. However, the BRKGA-GS tends to yield worse results compared to that obtained from the standard BRKGA.

  13. 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.

  14. Multiple Scattering of Laser Pulses in Snow Over Ice: Modeling the Potential Bias in ICESat Altimetry

    Science.gov (United States)

    Davis, A. B.; Varnai, T.; Marshak, A.

    2010-01-01

    The primary goal of NASA's current ICESat and future ICESat2 missions is to map the altitude of the Earth's land ice with high accuracy using laser altimetry technology, and to measure sea ice freeboard. Ice however is a highly transparent optical medium with variable scattering and absorption properties. Moreover, it is often covered by a layer of snow with varying depth and optical properties largely dependent on its age. We describe a modeling framework for estimating the potential altimetry bias caused by multiple scattering in the layered medium. We use both a Monte Carlo technique and an analytical diffusion model valid for optically thick media. Our preliminary numerical results are consistent with estimates of the multiple scattering delay from laboratory measurements using snow harvested in Greenland, namely, a few cm. Planned refinements of the models are described.

  15. A Dynamic Model for Limb Selection

    NARCIS (Netherlands)

    Cox, R.F.A; Smitsman, A.W.

    2008-01-01

    Two experiments and a model on limb selection are reported. In Experiment 1 left-handed and right-handed participants (N = 36) repeatedly used one hand for grasping a small cube. After a clear switch in the cube’s location, perseverative limb selection was revealed in both handedness groups. In

  16. Citation bias and selective focus on positive findings in the literature on the serotonin transporter gene (5-HTTLPR), life stress and depression

    NARCIS (Netherlands)

    Vries, de Ymkje Anna; Roest, A. M.; Franzen, M.; Munaf, M. R.; Bastiaansen, J. A.

    2016-01-01

    Background Caspi et al.'s 2003 report that 5-HTTLPR genotype moderates the influence of life stress on depression has been highly influential but remains contentious. We examined whether the evidence base for the 5-HTTLPR-stress interaction has been distorted by citation bias and a selective focus

  17. Accounting for Selectivity Bias and Correlation Across the Sequence From Elevated Blood Pressure to Hypertension Diagnosis and Treatment.

    Science.gov (United States)

    Gordon-Larsen, Penny; Attard, Samantha M; Howard, Annie Green; Popkin, Barry M; Zhang, Bing; Du, Shufa; Guilkey, David K

    2017-12-08

    It is unknown whether efforts to reduce hypertension burden in countries with very high prevalence, would be more effective if directed at hypertension diagnosis vs. treatment. Most analyses do not address bias and correlation across the sequence from elevated blood pressure (BP) to hypertension diagnosis and treatment, leading to potentially misleading findings. Using data spanning 18 years of the China Health and Nutrition Survey (n = 18,926; ages 18-75 years), we used an innovative 3-step, integrated system of equations to predict the sequence from: (i) elevated BP (systolic/diastolic BP ≥ 140/90 mm Hg) to (ii) diagnosed hypertension conditional on elevated BP, and to (iii) treatment (medication use) conditional on diagnosis, accounting for measured and unmeasured individual- and community-level confounders at each of the 3 steps. We compared results to separate traditional logistic regression models without control for unmeasured confounding. Using our 3-step model, elevated BP increased from 12.6% and 8.5% (1991) to 36.8% and 29% (2009) in men and women, respectively, but diagnosis remained under 50%. We found widening disparities in hypertension diagnosis (higher hypertension at lower vs. higher education (difference of 2% in 1991 that widened to 5% in 2009)) and narrowing disparities in education (difference of 6% in 1991 to 4% in 2009) and insurance status (difference of 7% in 1991 to 2% in 2009) for treatment. Our 3-step model improved model fit over traditionally used models. Our findings highlight serious barriers to hypertension diagnosis in Chinese adults, particularly among men and individuals of low attained education. © American Journal of Hypertension, Ltd 2017. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  18. Models of cultural niche construction with selection and assortative mating.

    Science.gov (United States)

    Creanza, Nicole; Fogarty, Laurel; Feldman, Marcus W

    2012-01-01

    Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.

  19. Models of cultural niche construction with selection and assortative mating.

    Directory of Open Access Journals (Sweden)

    Nicole Creanza

    Full Text Available Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.

  20. Review and selection of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)

    1994-04-04

    Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.

  1. Review and selection of unsaturated flow models

    International Nuclear Information System (INIS)

    Reeves, M.; Baker, N.A.; Duguid, J.O.

    1994-01-01

    Since the 1960's, ground-water flow models have been used for analysis of water resources problems. In the 1970's, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970's and well into the 1980's focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M ampersand O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M ampersand O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing

  2. 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.

  3. Graphical tools for model selection in generalized linear models.

    Science.gov (United States)

    Murray, K; Heritier, S; Müller, S

    2013-11-10

    Model selection techniques have existed for many years; however, to date, simple, clear and effective methods of visualising the model building process are sparse. This article describes graphical methods that assist in the selection of models and comparison of many different selection criteria. Specifically, we describe for logistic regression, how to visualize measures of description loss and of model complexity to facilitate the model selection dilemma. We advocate the use of the bootstrap to assess the stability of selected models and to enhance our graphical tools. We demonstrate which variables are important using variable inclusion plots and show that these can be invaluable plots for the model building process. We show with two case studies how these proposed tools are useful to learn more about important variables in the data and how these tools can assist the understanding of the model building process. Copyright © 2013 John Wiley & Sons, Ltd.

  4. The North Pacific Summer Jet and Climate Extremes Over North America: Mechanisms and Model Biases

    Science.gov (United States)

    Schubert, S. D.; Wang, H.; Chang, Y.; Koster, R. D.; Molod, A.

    2017-12-01

    The North Pacific summer jet (NPSJ) plays a critical role as a waveguide for weather systems and other sub-seasonal Rossby waves entering North America and therefore has a controlling influence on the warm season weather and climate extremes over much of the continent. In particular, much of the warm season precipitation that occurs over the central United States depends on subseasonal transients that are able to tap moisture from the Gulf of Mexico as they propagate across the continent. The GEOS-5 atmospheric general circulation model (AGCM), like many AGCMs, is deficient in the simulation of the NPSJ. It is shown that the deficiency is composed of: 1) a stunted jet in which the strongest winds are confined to the Asian continent, failing to extend across the North Pacific into the Gulf of Alaska as observed, and 2) a zonally symmetric poleward shift in the jet. These biases combine to impede the eastward propagation of the weather systems into the continent (the stunted jet), and deprive those systems that do enter the continent access to the moisture from the Gulf (the northward shift), leading to a dry bias over the central US. It is shown that the stunted jet bias is the result of too strong heating that occurs just south of the jet core over and near Tibet. Furthermore, it is shown that the poleward shift of the NPSJ can be corrected in the current GEOS-5 AGCM by increasing the vertical resolution. The implications of these results for improving warm season forecasts of extreme events will be discussed.

  5. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

  6. Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

    Directory of Open Access Journals (Sweden)

    M. B. Switanek

    2017-06-01

    Full Text Available Commonly used bias correction methods such as quantile mapping (QM assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM, have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM, which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

  7. Model selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidence.

    Science.gov (United States)

    Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang

    2014-12-01

    Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.

  8. The impact of healthcare visit timing on reported pertussis cough duration: Selection bias and disease pattern from reported cases in Michigan, USA, 2000-2010.

    Science.gov (United States)

    Knapp, Jennifer K; Wilson, Mark L; Murray, Susan; Boulton, Matthew L

    2016-09-29

    Pertussis is a potentially serious respiratory illness characterized by cough of exceptionally long duration of up to approximately100 days. While macrolide antibiotics are an effective treatment, there is an ongoing debate whether they also shorten the length of cough symptoms. We investigated whether public health surveillance data for pertussis, in which cases are identified at diagnosis, are potentially affected by selection bias and the possible consequences for reported cough duration. Data on 4,794 pertussis cases reported during 2000-2010 were extracted from the Michigan Disease Surveillance System, a statewide, web-based communicable disease reporting system, to specifically investigate increased duration of cough observed in pertussis patients with delayed initial healthcare visit. A simulated population of cases was derived from the observed surveillance data and truncated week-by-week to evaluate the effects of bias associated with stratification on timing of antibiotics. Cases presenting for medical evaluation later in the clinical course were more likely to have experienced delayed antibiotic therapy and longer average cough duration. A comparable magnitude of increasing cough duration was also observed in the simulated data. By stratifying on initial medical visit, selection bias effects based on timing of healthcare visit were demonstrated. Stratifying or controlling for the timing of the initial case identification and accompanying antibiotic treatment can create artificial patterns of observed cough duration. In surveillance data, differences in symptom duration may arise from selection bias and should not be presumed to be related to early antibiotic treatment.

  9. Using different Facebook advertisements to recruit men for an online mental health study: Engagement and selection bias

    Directory of Open Access Journals (Sweden)

    Isabella Choi

    2017-06-01

    Full Text Available A growing number of researchers are using Facebook to recruit for a range of online health, medical, and psychosocial studies. There is limited research on the representativeness of participants recruited from Facebook, and the content is rarely mentioned in the methods, despite some suggestion that the advertisement content affects recruitment success. This study explores the impact of different Facebook advertisement content for the same study on recruitment rate, engagement, and participant characteristics. Five Facebook advertisement sets (“resilience”, “happiness”, “strength”, “mental fitness”, and “mental health” were used to recruit male participants to an online mental health study which allowed them to find out about their mental health and wellbeing through completing six measures. The Facebook advertisements recruited 372 men to the study over a one month period. The cost per participant from the advertisement sets ranged from $0.55 to $3.85 Australian dollars. The “strength” advertisements resulted in the highest recruitment rate, but participants from this group were least engaged in the study website. The “strength” and “happiness” advertisements recruited more younger men. Participants recruited from the “mental health” advertisements had worse outcomes on the clinical measures of distress, wellbeing, strength, and stress. This study confirmed that different Facebook advertisement content leads to different recruitment rates and engagement with a study. Different advertisement also leads to selection bias in terms of demographic and mental health characteristics. Researchers should carefully consider the content of social media advertisements to be in accordance with their target population and consider reporting this to enable better assessment of generalisability.

  10. Selecting model complexity in learning problems

    Energy Technology Data Exchange (ETDEWEB)

    Buescher, K.L. [Los Alamos National Lab., NM (United States); Kumar, P.R. [Illinois Univ., Urbana, IL (United States). Coordinated Science Lab.

    1993-10-01

    To learn (or generalize) from noisy data, one must resist the temptation to pick a model for the underlying process that overfits the data. Many existing techniques solve this problem at the expense of requiring the evaluation of an absolute, a priori measure of each model`s complexity. We present a method that does not. Instead, it uses a natural, relative measure of each model`s complexity. This method first creates a pool of ``simple`` candidate models using part of the data and then selects from among these by using the rest of the data.

  11. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods

    Science.gov (United States)

    Teutschbein, Claudia; Seibert, Jan

    2012-08-01

    SummaryDespite the increasing use of regional climate model (RCM) simulations in hydrological climate-change impact studies, their application is challenging due to the risk of considerable biases. To deal with these biases, several bias correction methods have been developed recently, ranging from simple scaling to rather sophisticated approaches. This paper provides a review of available bias correction methods and demonstrates how they can be used to correct for deviations in an ensemble of 11 different RCM-simulated temperature and precipitation series. The performance of all methods was assessed in several ways: At first, differently corrected RCM data was compared to observed climate data. The second evaluation was based on the combined influence of corrected RCM-simulated temperature and precipitation on hydrological simulations of monthly mean streamflow as well as spring and autumn flood peaks for five catchments in Sweden under current (1961-1990) climate conditions. Finally, the impact on hydrological simulations based on projected future (2021-2050) climate conditions was compared for the different bias correction methods. Improvement of uncorrected RCM climate variables was achieved with all bias correction approaches. While all methods were able to correct the mean values, there were clear differences in their ability to correct other statistical properties such as standard deviation or percentiles. Simulated streamflow characteristics were sensitive to the quality of driving input data: Simulations driven with bias-corrected RCM variables fitted observed values better than simulations forced with uncorrected RCM climate variables and had more narrow variability bounds.

  12. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  13. Melody Track Selection Using Discriminative Language Model

    Science.gov (United States)

    Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong

    In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.

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

    Science.gov (United States)

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

    2017-05-01

    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 valid on half the trials or to decide whether the conclusion was believable on the other half. When belief and logic conflict, the default-interventionist view predicts that it should take less time to respond on the basis of belief than logic, and that the believability of a conclusion should interfere with judgments of validity, but not the reverse. The parallel-processing view predicts that beliefs should interfere with logic judgments only if the processing required to evaluate the logical structure exceeds that required to evaluate the knowledge necessary to make a belief-based judgment, and vice versa otherwise. Consistent with this latter view, for the simplest reasoning problems (modus ponens), judgments of belief resulted in lower accuracy than judgments of validity, and believability interfered more with judgments of validity than the converse. For problems of moderate complexity (modus tollens and single-model syllogisms), the interference was symmetrical, in that validity interfered with belief judgments to the same degree that believability interfered with validity judgments. For the most complex (three-term multiple-model syllogisms), conclusion believability interfered more with judgments of validity than vice versa, in spite of the significant interference from conclusion validity on judgments of belief.

  15. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Directory of Open Access Journals (Sweden)

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  16. 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

  17. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....

  18. 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.

  19. On the topographic bias and density distribution in modelling the geoid and orthometric heights

    Science.gov (United States)

    Sjöberg, Lars E.

    2018-03-01

    It is well known that the success in precise determinations of the gravimetric geoid height (N) and the orthometric height (H) rely on the knowledge of the topographic mass distribution. We show that the residual topographic bias due to an imprecise information on the topographic density is practically the same for N and H, but with opposite signs. This result is demonstrated both for the Helmert orthometric height and for a more precise orthometric height derived by analytical continuation of the external geopotential to the geoid. This result leads to the conclusion that precise gravimetric geoid heights cannot be validated by GNSS-levelling geoid heights in mountainous regions for the errors caused by the incorrect modelling of the topographic mass distribution, because this uncertainty is hidden in the difference between the two geoid estimators.

  20. Navigational efficiency in a biased and correlated random walk model of individual animal movement.

    Science.gov (United States)

    Bailey, Joseph D; Wallis, Jamie; Codling, Edward A

    2018-01-01

    Understanding how an individual animal is able to navigate through its environment is a key question in movement ecology that can give insight into observed movement patterns and the mechanisms behind them. Efficiency of navigation is important for behavioral processes at a range of different spatio-temporal scales, including foraging and migration. Random walk models provide a standard framework for modeling individual animal movement and navigation. Here we consider a vector-weighted biased and correlated random walk (BCRW) model for directed movement (taxis), where external navigation cues are balanced with forward persistence. We derive a mathematical approximation of the expected navigational efficiency for any BCRW of this form and confirm the model predictions using simulations. We demonstrate how the navigational efficiency is related to the weighting given to forward persistence and external navigation cues, and highlight the counter-intuitive result that for low (but realistic) levels of error on forward persistence, a higher navigational efficiency is achieved by giving more weighting to this indirect navigation cue rather than direct navigational cues. We discuss and interpret the relevance of these results for understanding animal movement and navigation strategies. © 2017 by the Ecological Society of America.

  1. On spatial mutation-selection models

    Energy Technology Data Exchange (ETDEWEB)

    Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Minlos, Robert, E-mail: minl@iitp.ru; Pirogov, Sergey, E-mail: pirogov@proc.ru [IITP, RAS, Bolshoi Karetnyi 19, Moscow (Russian Federation)

    2013-11-15

    We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.

  2. 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

  3. Test Bias and the Elimination of Racism

    Science.gov (United States)

    Sedlacek, William E.

    1977-01-01

    Three types of test bias are discussed: content bias, atmosphere bias, and use bias. Use bias is considered the most important. Tests reflect the bias in society, and eliminating test bias means eliminating racism and sexism in society. A six-stage model to eliminate racism and sexism is presented. (Author)

  4. 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

  5. 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.

  6. Analytical recovery of protozoan enumeration methods: have drinking water QMRA models corrected or created bias?

    Science.gov (United States)

    Schmidt, P J; Emelko, M B; Thompson, M E

    2013-05-01

    Quantitative microbial risk assessment (QMRA) is a tool to evaluate the potential implications of pathogens in a water supply or other media and is of increasing interest to regulators. In the case of potentially pathogenic protozoa (e.g. Cryptosporidium oocysts and Giardia cysts), it is well known that the methods used to enumerate (oo)cysts in samples of water and other media can have low and highly variable analytical recovery. In these applications, QMRA has evolved from ignoring analytical recovery to addressing it in point-estimates of risk, and then to addressing variation of analytical recovery in Monte Carlo risk assessments. Often, variation of analytical recovery is addressed in exposure assessment by dividing concentration values that were obtained without consideration of analytical recovery by random beta-distributed recovery values. A simple mathematical proof is provided to demonstrate that this conventional approach to address non-constant analytical recovery in drinking water QMRA will lead to overestimation of mean pathogen concentrations. The bias, which can exceed an order of magnitude, is greatest when low analytical recovery values are common. A simulated dataset is analyzed using a diverse set of approaches to obtain distributions representing temporal variation in the oocyst concentration, and mean annual risk is then computed from each concentration distribution using a simple risk model. This illustrative example demonstrates that the bias associated with mishandling non-constant analytical recovery and non-detect samples can cause drinking water systems to be erroneously classified as surpassing risk thresholds. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Sparse model selection via integral terms

    Science.gov (United States)

    Schaeffer, Hayden; McCalla, Scott G.

    2017-08-01

    Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.

  8. Adverse selection model regarding tobacco consumption

    Directory of Open Access Journals (Sweden)

    Dumitru MARIN

    2006-01-01

    Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.

  9. Media Bias

    OpenAIRE

    Sendhil Mullainathan; Andrei Shleifer

    2002-01-01

    There are two different types of media bias. One bias, which we refer to as ideology, reflects a news outlet's desire to affect reader opinions in a particular direction. The second bias, which we refer to as spin, reflects the outlet's attempt to simply create a memorable story. We examine competition among media outlets in the presence of these biases. Whereas competition can eliminate the effect of ideological bias, it actually exaggerates the incentive to spin stories.

  10. How many dinosaur species were there? Fossil bias and true richness estimated using a Poisson sampling model.

    Science.gov (United States)

    Starrfelt, Jostein; Liow, Lee Hsiang

    2016-04-05

    The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has also inherent biases due to geological, physical, chemical and biological factors. Our knowledge of past life is also biased because of differences in academic and amateur interests and sampling efforts. As a result, not all individuals or species that lived in the past are equally likely to be discovered at any point in time or space. To reconstruct temporal dynamics of diversity using the fossil record, biased sampling must be explicitly taken into account. Here, we introduce an approach that uses the variation in the number of times each species is observed in the fossil record to estimate both sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling model) and explore its robustness to violation of its assumptions via simulations. We then venture to estimate sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we estimate that 1936 (1543-2468) species of dinosaurs roamed the Earth during the Mesozoic. We also present improved estimates of species richness trajectories of the three major dinosaur clades: the sauropodomorphs, ornithischians and theropods, casting doubt on the Jurassic-Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable sampling bias throughout the Mesozoic. © 2016 The Authors.

  11. Modeling and Selection of Software Service Variants

    OpenAIRE

    Wittern, John Erik

    2015-01-01

    Providers and consumers have to deal with variants, meaning alternative instances of a service?s design, implementation, deployment, or operation, when developing or delivering software services. This work presents service feature modeling to deal with associated challenges, comprising a language to represent software service variants and a set of methods for modeling and subsequent variant selection. This work?s evaluation includes a POC implementation and two real-life use cases.

  12. Understanding selection bias, time-lags and measurement bias in secondary data sources: Putting the Encyclopedia of Associations database in broader context.

    Science.gov (United States)

    Bevan, Shaun; Baumgartner, Frank R; Johnson, Erik W; McCarthy, John D

    2013-11-01

    Secondary data gathered for purposes other than research play an important role in the social sciences. A recent data release has made an important source of publicly available data on associational interests, the Encyclopedia of Associations (EA), readily accessible to scholars (www.policyagendas.org). In this paper we introduce these new data and systematically investigate issues of lag between events and subsequent reporting in the EA, as these have important but under-appreciated effects on time-series statistical models. We further analyze the accuracy and coverage of the database in numerous ways. Our study serves as a guide to potential users of this database, but we also reflect upon a number of issues that should concern all researchers who use secondary data such as newspaper records, IRS reports and FBI Uniform Crime Reports. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Model Selection in Data Analysis Competitions

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Winther, Ole

    2014-01-01

    The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platfor...

  14. Efficiently adapting graphical models for selectivity estimation

    DEFF Research Database (Denmark)

    Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.

    2013-01-01

    in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...

  15. Characterization and modeling of organic (P3HT:PCBM) solar cells as a function of bias and illumination

    DEFF Research Database (Denmark)

    Rizzo, Antonio; Cester, Andrea; Wrachien, Nicola

    2016-01-01

    We investigated the response of roll coated organic solar cells at different bias voltages and illumination levels to implement a detailed impedance model. The technique used for the investigation is based on the combination of standard DC characterization with the impedance spectroscopy at diffe......We investigated the response of roll coated organic solar cells at different bias voltages and illumination levels to implement a detailed impedance model. The technique used for the investigation is based on the combination of standard DC characterization with the impedance spectroscopy...

  16. Using ARM Measurements to Understand and Reduce the Double ITCZ Biases in the Community Atmospheric Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Minghua [Stony Brook Univ., NY (United States)

    2016-12-08

    that features a spurious double Inter-tropical Convergence Zone (ITCZ) in most models. This work is recently published in Yu et al. (2016). Interannual variation of ITCZ south of the eastern equatorial Pacific: By analyzing data from satellites, field measurements and atmospheric reanalysis, we have characterized the interannual variation of boreal spring precipitation in the eastern tropical Pacific and found the cause of the observed interannual variability. We have shown that ITCZ in this region can occur as a single ITCZ along the Equator, single ITCZ north of the Equator, single ITCZ south of the Equator, and double ITCZ on both sides of the Equator. We have found that convective instability only plays a secondary role in the ITCZ interannual variability. Instead, the remote impact of the Pacific basin-wide SST on the horizontal gradient of surface pressure and wind convergence is the primary driver of this interannual variability. Results point to the need to include moisture convergence in convection schemes to improve the simulation of precipitation in the eastern tropical Pacific. This result has been recently submitted for publication (Yu and Zhang 2016). 2. Improvement of model parameterizations to reduce the double ITCZ bias We analyzed the current status of climate model performance in simulating precipitation in the equatorial Pacific. We have found that the double ITCZ bias has not been reduced in CMIP5 models relative to CMIP4 models. We have characterized the dynamic structure of the common bias by using precipitation, sea surface temperature, surface winds and sea-level. Results are published in Zhang et al. (2015): Since cumulus convection plays a significant role in the double ITCZ behavior in models, we have used measurements from ARM and other sources to carry out a systematic analysis of the roles of shallow and deep convection in the CAM. We found that in both CAM4 and CAM5, when the intensity of deep convection decreases as a result of

  17. Minimum Bias Interaction Triggers in ATLAS

    CERN Document Server

    Kwee, R E; The ATLAS collaboration

    2010-01-01

    Since the restart of the LHC in November 2009, ATLAS has collected inelastic ppcollisions 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 < |eta| < 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 presente...

  18. Model selection criterion in survival analysis

    Science.gov (United States)

    Karabey, Uǧur; Tutkun, Nihal Ata

    2017-07-01

    Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.

  19. On Using Selection Procedures with Binomial Models.

    Science.gov (United States)

    1983-10-01

    eds.), Shinko Tsusho Co. Ltd., Tokyo, Japan , pp. 501-533. Gupta, S. S. and Sobel, M. (1960). Selecting a subset containing the best of several...IA_____3_6r__I____ *TITLE food A$ieweI L TYPE of 09PORT 6 PERIOD COVERED ON USING SELECTION PROCEDURES WITH BINOMIAL MODELS Technical 6. PeSPRFeauS1 ONG. REPORT...ontoedis stoc toeSI. to Ei.,..,t&* toemR.,. 14. SUPPOLEMENTARY MOCTES 19. Rey WORDS (Coatiou. 40 ow.oa* edo if Necesary and #do""&a by block number

  20. Aerosol model selection and uncertainty modelling by adaptive MCMC technique

    Directory of Open Access Journals (Sweden)

    M. Laine

    2008-12-01

    Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.

    The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.

    We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.

  1. Review and selection of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1993-09-10

    Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer ground-water flow models; to conduct performance assessments; and to develop performance assessment models, where necessary. In the area of scientific modeling, the M&O CRWMS has the following responsibilities: To provide overall management and integration of modeling activities. To provide a framework for focusing modeling and model development. To identify areas that require increased or decreased emphasis. To ensure that the tools necessary to conduct performance assessment are available. These responsibilities are being initiated through a three-step process. It consists of a thorough review of existing models, testing of models which best fit the established requirements, and making recommendations for future development that should be conducted. Future model enhancement will then focus on the models selected during this activity. Furthermore, in order to manage future model development, particularly in those areas requiring substantial enhancement, the three-step process will be updated and reported periodically in the future.

  2. 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.

  3. A Language for Modeling Cultural Norms, Biases and Stereotypes for Human Behavior Models

    National Research Council Canada - National Science Library

    Solomon, Steven; van Lent, Michael; Core, Mark; Carpenter, Paul; Rosenberg, Milton

    2008-01-01

    .... The Culturally-Affected Behavior project seeks to define a language for encoding ethnographic data in order to capture cultural knowledge and use that knowledge to affect human behavior models...

  4. 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.

  5. 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

  6. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  7. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon

    2015-12-21

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  8. Chemical identification using Bayesian model selection

    Energy Technology Data Exchange (ETDEWEB)

    Burr, Tom; Fry, H. A. (Herbert A.); McVey, B. D. (Brian D.); Sander, E. (Eric)

    2002-01-01

    Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all chemical species present. This leads to a generalized least squares problem in which we focus on 'subset selection' to identify the chemicals thought to be present. Bayesian model selection allows us to approximate the posterior probability that each chemical in the library is present by adding the posterior probabilities of all the subsets which include the chemical. We present results using realistic simulated data for the case with 1 to 5 chemicals present in each target and compare performance to a hybrid of forward and backward stepwise selection procedure using the F statistic.

  9. Expatriates Selection: An Essay of Model Analysis

    Directory of Open Access Journals (Sweden)

    Rui Bártolo-Ribeiro

    2015-03-01

    Full Text Available The business expansion to other geographical areas with different cultures from which organizations were created and developed leads to the expatriation of employees to these destinations. Recruitment and selection procedures of expatriates do not always have the intended success leading to an early return of these professionals with the consequent organizational disorders. In this study, several articles published in the last five years were analyzed in order to identify the most frequently mentioned dimensions in the selection of expatriates in terms of success and failure. The characteristics in the selection process that may increase prediction of adaptation of expatriates to new cultural contexts of the some organization were studied according to the KSAOs model. Few references were found concerning Knowledge, Skills and Abilities dimensions in the analyzed papers. There was a strong predominance on the evaluation of Other Characteristics, and was given more importance to dispositional factors than situational factors for promoting the integration of the expatriates.

  10. Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates.

    Directory of Open Access Journals (Sweden)

    Andreas Tuerk

    2017-05-01

    Full Text Available Accuracy of transcript quantification with RNA-Seq is negatively affected by positional fragment bias. This article introduces Mix2 (rd. "mixquare", a transcript quantification method which uses a mixture of probability distributions to model and thereby neutralize the effects of positional fragment bias. The parameters of Mix2 are trained by Expectation Maximization resulting in simultaneous transcript abundance and bias estimates. We compare Mix2 to Cufflinks, RSEM, eXpress and PennSeq; state-of-the-art quantification methods implementing some form of bias correction. On four synthetic biases we show that the accuracy of Mix2 overall exceeds the accuracy of the other methods and that its bias estimates converge to the correct solution. We further evaluate Mix2 on real RNA-Seq data from the Microarray and Sequencing Quality Control (MAQC, SEQC Consortia. On MAQC data, Mix2 achieves improved correlation to qPCR measurements with a relative increase in R2 between 4% and 50%. Mix2 also yields repeatable concentration estimates across technical replicates with a relative increase in R2 between 8% and 47% and reduced standard deviation across the full concentration range. We further observe more accurate detection of differential expression with a relative increase in true positives between 74% and 378% for 5% false positives. In addition, Mix2 reveals 5 dominant biases in MAQC data deviating from the common assumption of a uniform fragment distribution. On SEQC data, Mix2 yields higher consistency between measured and predicted concentration ratios. A relative error of 20% or less is obtained for 51% of transcripts by Mix2, 40% of transcripts by Cufflinks and RSEM and 30% by eXpress. Titration order consistency is correct for 47% of transcripts for Mix2, 41% for Cufflinks and RSEM and 34% for eXpress. We, further, observe improved repeatability across laboratory sites with a relative increase in R2 between 8% and 44% and reduced standard deviation.

  11. 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.

  12. 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

  13. 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)

  14. Potential Bias in Projecting Future Regional Megadrought Risk: Insights From A Global Data-Model Framework

    Science.gov (United States)

    Overpeck, J. T.; Ault, T.; Cole, J. E.; Fasullo, J.; Loope, G. R.; Parsons, L. A.; Stevenson, S.

    2015-12-01

    Megadrought is one of the most significant and costly climate extremes, and one that stakeholders (e.g., water and other resource managers) the world over wish to understand better; in particular, they need estimates of the risk of severe droughts as a function of drought frequency, severity, duration, and atmospheric greenhouse gas concentration. In many dry-climate regions of the globe, megadrought is synonymous with multi-decadal drought. However, in other regions, megadrought can be defined as extended drought, mostly not seen in the period of instrumental observations, and that would have large impacts if it were to occur in the future. New and published paleoclimatic observations allow us to understand the spectrum of drought in many regions of the globe; droughts exceeding 50 years have occurred in recent Earth history in southwestern North America, sub-Saharan Africa, the Mediterranean and Australia, whereas shorter megadroughts have occurred in Monsoon Asia, Amazonia and elsewhere. Data-model comparisons for regions with sufficiently long (e.g., 1000-2000 years) records of observed hydroclimatic variability suggest that state-of-the-art models can provide realistic estimates of interannual to decadal drought risk, but underestimate the risk of megadrought. Likely reasons for this shortcoming are the lack of sufficient multi-decadal variability in simulations of the past and future, plus an underappreciated understanding about how temperature variability and land-surface feedbacks interact with hydrological and ecological drought, as well as the roles played by unusually wet hydroclimatic extremes (e.g., ENSO related) in ending droughts of long duration. Paleoclimatic records also provide the opportunity to estimate how much models underestimate megadrought risk as a function of locale, frequency, severity, duration, and atmospheric greenhouse gas concentration; they also aid in providing stakeholders with bias-corrected estimates of megadrought risk.

  15. Biases in Long-term NO2 Averages Inferred from Satellite Observations Due to Cloud Selection Criteria

    Science.gov (United States)

    Geddes, Jeffrey A.; Murphy, Jennifer G.; O'Brien, Jason M.; Celarier, Edward A.

    2012-01-01

    Retrievals of atmospheric trace gas column densities from space are compromised by the presence of clouds, requiring most studies to exclude observations with significant cloud fractions in the instrument's field of view. Using NO2 observations at three ground stations representing urban, suburban, and rural environments, and tropospheric vertical column densities measured by the Ozone Monitoring Instrument (OMI) over each site, we show that the observations from space represent monthly averaged ground-level pollutant conditions well (R=0.86) under relatively cloud-free conditions. However, by analyzing the ground-level data and applying the OMI cloud fraction as a filter, we show there is a significant bias in long-term averaged NO2 as a result of removing the data during cloudy conditions. For the ground-based sites considered in this study, excluding observations on days when OMI-derived cloud fractions were greater than 0.2 causes 12:00-14:00 mean summer mixing ratios to be underestimated by 12%+/-6%, 20%+/-7%, and 40%+/-10% on average (+/-1 standard deviation) at the urban, suburban, and rural sites respectively. This bias was investigated in particular at the rural site, a region where pollutant transport is the main source of NO2, and where longterm observations of NOy were also available. Evidence of changing photochemical conditions and a correlation between clear skies and the transport of cleaner air masses play key roles in explaining the bias. The magnitude of a bias is expected to vary from site to site depending on meteorology and proximity to NOx sources, and decreases when longer averaging times of ground station data (e.g. 24-h) are used for the comparison.

  16. Reserve selection using nonlinear species distribution models.

    Science.gov (United States)

    Moilanen, Atte

    2005-06-01

    Reserve design is concerned with optimal selection of sites for new conservation areas. Spatial reserve design explicitly considers the spatial pattern of the proposed reserve network and the effects of that pattern on reserve cost and/or ability to maintain species there. The vast majority of reserve selection formulations have assumed a linear problem structure, which effectively means that the biological value of a potential reserve site does not depend on the pattern of selected cells. However, spatial population dynamics and autocorrelation cause the biological values of neighboring sites to be interdependent. Habitat degradation may have indirect negative effects on biodiversity in areas neighboring the degraded site as a result of, for example, negative edge effects or lower permeability for animal movement. In this study, I present a formulation and a spatial optimization algorithm for nonlinear reserve selection problems in grid-based landscapes that accounts for interdependent site values. The method is demonstrated using habitat maps and nonlinear habitat models for threatened birds in the Netherlands, and it is shown that near-optimal solutions are found for regions consisting of up to hundreds of thousands grid cells, a landscape size much larger than those commonly attempted even with linear reserve selection formulations.

  17. Investigation of selection bias in the association of race with prevalent atrial fibrillation in a national cohort study: REasons for Geographic And Racial Differences in Stroke (REGARDS).

    Science.gov (United States)

    Thacker, Evan L; Soliman, Elsayed Z; Pulley, LeaVonne; Safford, Monika M; Howard, George; Howard, Virginia J

    2016-08-01

    Atrial fibrillation (AF) is diagnosed more commonly in whites than blacks in the United States. In epidemiologic studies, selection bias could induce a noncausal positive association of white race with prevalent AF if voluntary enrollment was influenced by both race and AF status. We investigated whether nonrandom enrollment biased the association of race with prevalent self-reported AF in the US-based REasons for Geographic And Racial Differences in Stroke Study (REGARDS). REGARDS had a two-stage enrollment process, allowing us to compare 30,183 fully enrolled REGARDS participants with 12,828 people who completed the first-stage telephone survey but did not complete the second-stage in-home visit to finalize their REGARDS enrollment (telephone-only participants). REGARDS enrollment was higher among whites (77.1%) than among blacks (62.3%) but did not differ by self-reported AF status. The prevalence of AF was 8.45% in whites and 5.86% in blacks adjusted for age, sex, income, education, and perceived general health. The adjusted white/black prevalence ratio of self-reported AF was 1.43 (95% CI, 1.32-1.56) among REGARDS participants and 1.38 (1.22-1.55) among telephone-only participants. These findings suggest that selection bias is not a viable explanation for the higher prevalence of self-reported AF among whites in population studies such as REGARDS. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  19. Multi-dimensional model order selection

    Directory of Open Access Journals (Sweden)

    Roemer Florian

    2011-01-01

    Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.

  20. Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections

    Directory of Open Access Journals (Sweden)

    Fei Guo

    2016-06-01

    Full Text Available The BeiDou satellite-induced code biases have been confirmed to be orbit type-, frequency-, and elevation-dependent. Such code-phase divergences (code bias variations severely affect absolute precise applications which use code measurements. To reduce their adverse effects, an improved correction model is proposed in this paper. Different from the model proposed by Wanninger and Beer (2015, more datasets (a time span of almost two years were used to produce the correction values. More importantly, the stochastic information, i.e., the precision indexes, were given together with correction values in the improved model. However, only correction values were given while the precision indexes were completely missing in the traditional model. With the improved correction model, users may have a better understanding of their corrections, especially the uncertainty of corrections. Thus, it is helpful for refining the stochastic model of code observations. Validation tests in precise point positioning (PPP reveal that a proper stochastic model is critical. The actual precision of the corrected code observations can be reflected in a more objective manner if the stochastic model of the corrections is taken into account. As a consequence, PPP solutions with the improved model outperforms the traditional one in terms of positioning accuracy, as well as convergence speed. In addition, the Melbourne-Wübbena (MW combination which serves for ambiguity fixing were verified as well. The uncorrected MW values show strong systematic variations with an amplitude of half a wide-lane cycle, which prevents precise ambiguity determination and successful ambiguity resolution. After application of the code bias correction models, the systematic variations can be greatly removed, and the resulting wide lane ambiguities are more likely to be fixed. Moreover, the code residuals show more reasonable distributions after code bias corrections with either the traditional or the

  1. Semiparametric model and inference for spontaneous abortion data with a cured proportion and biased sampling.

    Science.gov (United States)

    Piao, Jin; Ning, Jing; Chambers, Christina D; Xu, Ronghui

    2018-01-01

    Evaluating and understanding the risk and safety of using medications for autoimmune disease in a woman during her pregnancy will help both clinicians and pregnant women to make better treatment decisions. However, utilizing spontaneous abortion (SAB) data collected in observational studies of pregnancy to derive valid inference poses two major challenges. First, the data from the observational cohort are not random samples of the target population due to the sampling mechanism. Pregnant women with early SAB are more likely to be excluded from the cohort, and there may be substantial differences between the observed SAB time and those in the target population. Second, the observed data are heterogeneous and contain a "cured" proportion. In this article, we consider semiparametric models to simultaneously estimate the probability of being cured and the distribution of time to SAB for the uncured subgroup. To derive the maximum likelihood estimators, we appropriately adjust the sampling bias in the likelihood function and develop an expectation-maximization algorithm to overcome the computational challenge. We apply the empirical process theory to prove the consistency and asymptotic normality of the estimators. We examine the finite sample performance of the proposed estimators in simulation studies and illustrate the proposed method through an application to SAB data from pregnant women. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  2. Regulation of an Autoimmune Model for Multiple Sclerosis in Th2-Biased GATA3 Transgenic Mice

    Directory of Open Access Journals (Sweden)

    Viromi Fernando

    2014-01-01

    Full Text Available T helper (Th2 cells have been proposed to play a neuroprotective role in multiple sclerosis (MS. This is mainly based on “loss-of-function” studies in an animal model for MS, experimental autoimmune encephalomyelitis (EAE, using blocking antibodies against Th2 related cytokines, and knockout mice lacking Th2-related molecules. We tested whether an increase of Th2 responses (“gain-of-function” approach could alter EAE, the approach of novel GATA binding protein 3 (GATA3-transgenic (tg mice that overexpress GATA3, a transcription factor required for Th2 differentiation. In EAE induced with myelin oligodendrocyte glycoprotein (MOG35−55 peptide, GATA3-tg mice had a significantly delayed onset of disease and a less severe maximum clinical score, compared with wild-type C57BL/6 mice. Histologically, GATA3-tg mice had decreased levels of meningitis and demyelination in the spinal cord, and anti-inflammatory cytokine profiles immunologically, however both groups developed similar levels of MOG-specific lymphoproliferative responses. During the early stage, we detected higher levels of interleukin (IL-4 and IL-10, with MOG and mitogen stimulation of regional lymph node cells in GATA3-tg mice. During the late stage, only mitogen stimulation induced higher IL-4 and lower interferon-γ and IL-17 production in GATA3-tg mice. These results suggest that a preexisting bias toward a Th2 immune response may reduce the severity of inflammatory demyelinating diseases, including MS.

  3. A protocol to identify and minimise selection and information bias in abattoir surveys estimating prevalence, using Fasciola hepatica as an example.

    Science.gov (United States)

    Carroll, Rebecca I; Forbes, Andrew; Graham, David A; Messam, Locksley L McV

    2017-09-01

    Abattoir surveys and findings from post-mortem meat inspection are commonly used to estimate infection or disease prevalence in farm animal populations. However, the function of an abattoir is to slaughter animals for human consumption, and the collection of information on animal health for research purposes is a secondary objective. This can result in methodological shortcomings leading to biased prevalence estimates. Selection bias can occur when the study population as obtained from the abattoir is not an accurate representation of the target population. Virtually all of the tests used in abattoir surveys to detect infections or diseases that impact animal health are imperfect, leading to errors in identifying the outcome of interest and consequently, information bias. Examination of abattoir surveys estimating prevalence in the literature reveals shortcomings in the methods used in these studies. While the STROBE-Vet statement provides clear guidance on the reporting of observational research, we have not found any guidelines in the literature advising researchers on how to conduct abattoir surveys. This paper presents a protocol in two flowcharts to help researchers (regardless of their background in epidemiology) to first identify, and, where possible, minimise biases in abattoir surveys estimating prevalence. Flowchart 1 examines the identification of the target population and the appropriate study population while Flowchart 2 guides the researcher in identifying, and, where possible, correcting potential sources of outcome misclassification. Examples of simple sensitivity analyses are also presented which approximate the likely uncertainty in prevalence estimates due to systematic errors. Finally, the researcher is directed to outline any limitations of the study in the discussion section of the paper. This protocol makes it easier to conduct an abattoir survey using sound methods, identifying and, where possible, minimizing biases. Copyright © 2017

  4. A simple parametric model selection test

    OpenAIRE

    Susanne M. Schennach; Daniel Wilhelm

    2014-01-01

    We propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly speci fied or misspeci fied, they may be nested, non-nested, strictly non-nested or overlapping. Unlike in previous testing approaches, no pre-testing is needed, since in each case, the same test statistic to...

  5. Novel metrics for growth model selection.

    Science.gov (United States)

    Grigsby, Matthew R; Di, Junrui; Leroux, Andrew; Zipunnikov, Vadim; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William

    2018-01-01

    Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria. Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE). Predictive performance metrics were used to compare linear mixed effects models and functional regression models. Prediction accuracy was assessed by partitioning the observed data into training and test datasets. This partitioning was constructed to assess prediction accuracy for backward (i.e., early growth), forward (i.e., late growth), in-range, and on new-individuals. Analyses were done with height measurements from 215 Peruvian children with data spanning from near birth to 2 years of age. Functional models outperformed linear mixed effects models in all scenarios tested. In particular, prediction errors for functional concurrent regression (FCR) and functional principal component analysis models were approximately 6% lower when compared to linear mixed effects models. When we weighted subject-specific MSEs according to subject-specific growth rates during infancy, we found that FCR was the best performer in all scenarios. With this novel approach, we can quantitatively compare non-nested models and weight subgroups of interest to select the best performing growth model for a particular application or problem at hand.

  6. 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.

  7. Validity of Differently Bias-Corrected Regional Climate Model Simulations for Streamflow Simulations under Changing Climate Conditions

    Science.gov (United States)

    Teutschbein, C.; Seibert, J.

    2012-04-01

    The direct application of Regional Climate Model (RCM) simulations in hydrological climate-change impact studies can be questionable due to the potential risk for considerable biases. Several bias correction approaches - ranging from simple scaling to rather sophisticated methods - have been developed to help impact modelers coping with the various problems linked to biased RCM output. The main disadvantage of any of these correction procedures is their underlying assumption of stationarity: the correction algorithm and its parameterization for current climate are expected to also be valid for future climate conditions. Whether or not this presupposition is actually fulfilled for future conditions cannot be evaluated - given our lack of time machines. Nevertheless, systematic testing of how well bias correction procedures perform for conditions different from those used for calibration can be done by applying a differential split-sample as proposed by Klemeš ["Operational testing of hydrological simulation models", Hydrological Sciences Journal 31, no. 1 (1986): 13-24]. This contribution summarizes shortly available bias correction methods and demonstrates their application using the example of an ensemble of 11 different RCM-simulated temperature and precipitation series. We then applied a differential split-sample test which enabled us to evaluate the performance of different bias correction procedures under changing climate conditions with only a limited amount of data (30-year records). Furthermore, we evaluated the different correction methods based on their combined influence on hydrological simulations of monthly mean streamflow as well as spring and autumn flood peaks for five meso-scale catchments in Sweden under current (1961-1990) and future (2021-2050) climate conditions. This differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation based on an independent data set. More

  8. 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.

  9. 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).

  10. Bias correction for the proportional odds logistic regression model with application to a study of surgical complications.

    Science.gov (United States)

    Lipsitz, Stuart R; Fitzmaurice, Garrett M; Regenbogen, Scott E; Sinha, Debajyoti; Ibrahim, Joseph G; Gawande, Atul A

    2013-03-01

    The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is relatively small, and/or certain outcome categories are rare, maximum likelihood can yield biased estimates of the regression parameters. Firth (1993) and Kosmidis and Firth (2009) proposed a procedure to remove the leading term in the asymptotic bias of the maximum likelihood estimator. Their approach is most easily implemented for univariate outcomes. In this paper, we derive a bias correction that exploits the proportionality between Poisson and multinomial likelihoods for multinomial regression models. Specifically, we describe a bias correction for the proportional odds logistic regression model, based on the likelihood from a collection of independent Poisson random variables whose means are constrained to sum to 1, that is straightforward to implement. The proposed method is motivated by a study of predictors of post-operative complications in patients undergoing colon or rectal surgery (Gawande et al., 2007).

  11. Less healthy, but more active: Opposing selection biases when recruiting older people to a physical activity study through primary care

    Directory of Open Access Journals (Sweden)

    Carey Iain M

    2008-05-01

    Full Text Available Abstract Background Physical activity studies in older people experience poor recruitment. We wished to assess the influence of activity levels and health status on recruitment to a physical activity study in older people. Methods Comparison of participants and non-participants to a physical activity study using accelerometers in patients aged ≥ 65 years registered with a UK primary care centre. Logistic regression was used to calculate odds ratios (OR of participants in the accelerometer study with various adjustments. Analyses were initially adjusted for age, sex and household clustering; the health variables were then adjusted for physical activity levels and vice versa to look for independent effects. Results 43%(240/560 participated in the physical activity study. Age had no effect but males were more likely to participate than females OR 1.4(1.1–1.8. 46% (76/164 of non-participants sent the questionnaire returned it. The 240 participants reported greater physical activity than the 76 non-participants on all measures, eg faster walking OR 3.2(1.4–7.7, or 10.4(3.2–33.3 after adjustment for health variables. Participants reported more health problems; this effect became statistically significant after controlling for physical activity, eg disability OR 2.4(1.1–5.1. Conclusion Physical activity studies on older primary care patients may experience both a strong bias towards participants being more active and a weaker bias towards participants having more health problems and therefore primary care contact. The latter bias could be advantageous for physical activity intervention studies, where those with health problems need targeting.

  12. Using Inverse Probability Bootstrap Sampling to Eliminate Sample Induced Bias in Model Based Analysis of Unequal Probability Samples.

    Directory of Open Access Journals (Sweden)

    Matthew Nahorniak

    Full Text Available In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the sampling design phase. Questions may arise requiring the use of model based statistical tools such as multiple regression, quantile regression, or regression tree analysis. However, such model based tools may require, for ensuring unbiased estimation, data from simple random samples, which can be problematic when analyzing data from unequal probability designs. Despite numerous method specific tools available to properly account for sampling design, too often in the analysis of ecological data, sample design is ignored and consequences are not properly considered. We demonstrate here that violation of this assumption can lead to biased parameter estimates in ecological research. In addition, to the set of tools available for researchers to properly account for sampling design in model based analysis, we introduce inverse probability bootstrapping (IPB. Inverse probability bootstrapping is an easily implemented method for obtaining equal probability re-samples from a probability sample, from which unbiased model based estimates can be made. We demonstrate the potential for bias in model-based analyses that ignore sample inclusion probabilities, and the effectiveness of IPB sampling in eliminating this bias, using both simulated and actual ecological data. For illustration, we considered three model based analysis tools--linear regression, quantile regression, and boosted regression tree analysis. In all models, using both simulated and actual ecological data, we

  13. On commercial media bias

    OpenAIRE

    Germano, Fabrizio

    2008-01-01

    Within the spokes model of Chen and Riordan (2007) that allows for non-localized competition among arbitrary numbers of media outlets, we quantify the effect of concentration of ownership on quality and bias of media content. A main result shows that too few commercial outlets, or better, too few separate owners of commercial outlets can lead to substantial bias in equilibrium. Increasing the number of outlets (commercial and non-commercial) tends to bring down this bias; but the strongest ef...

  14. A study of the Pythia 8 description of ATLAS minimum bias measurements with the Donnachie-Landshoff diffractive model

    CERN Document Server

    The ATLAS collaboration

    2016-01-01

    We present a new tune of the Pythia8 event generator, titled ``A3'' and suitable for inclusive QCD modelling, including minimum bias physics and pile-up overlay. The tuning uses the early Run~2 charged particle distribution and inelastic cross section results from ATLAS in addition to the Run~1 data used to construct previous minimum-bias tunes. For the first time, the tuning included a consideration of diffraction modelling parameters and a diffractive model other than the Pythia8 default is used in the final configuration. That resulted in a better descriptions of the measured inelastic cross-sections, and similar or better level of agreement compared to the currently used A2 tune for other distributions considered.

  15. 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

  16. 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

  17. Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.

    Science.gov (United States)

    Groth, Detlef

    2017-04-01

    Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2018-01-01

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

  20. How does dynamical downscaling affect model biases and future projections of explosive extratropical cyclones along North America's Atlantic coast?

    Science.gov (United States)

    Seiler, C.; Zwiers, F. W.; Hodges, K. I.; Scinocca, J. F.

    2018-01-01

    Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America's Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America's Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (-0.1 day^{-1}). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to -22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (-15%, -18%) and Eady growth rate (-0.2 day^{-1}, -0.2 day^{-1}), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.

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

    Science.gov (United States)

    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.

  2. Model selection and comparison for independents sinusoids

    DEFF Research Database (Denmark)

    Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt

    2014-01-01

    In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve this me....... Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online.......In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve...... this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion...

  3. Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

    Science.gov (United States)

    Cannon, Alex J.

    2018-01-01

    Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin

  4. Essays in energy policy and planning modeling under uncertainty: Value of information, optimistic biases, and simulation of capacity markets

    Science.gov (United States)

    Hu, Ming-Che

    Optimization and simulation are popular operations research and systems analysis tools for energy policy modeling. This dissertation addresses three important questions concerning the use of these tools for energy market (and electricity market) modeling and planning under uncertainty. (1) What is the value of information and cost of disregarding different sources of uncertainty for the U.S. energy economy? (2) Could model-based calculations of the performance (social welfare) of competitive and oligopolistic market equilibria be optimistically biased due to uncertainties in objective function coefficients? (3) How do alternative sloped demand curves perform in the PJM capacity market under economic and weather uncertainty? How does curve adjustment and cost dynamics affect the capacity market outcomes? To address the first question, two-stage stochastic optimization is utilized in the U.S. national MARKAL energy model; then the value of information and cost of ignoring uncertainty are estimated for three uncertainties: carbon cap policy, load growth and natural gas prices. When an uncertainty is important, then explicitly considering those risks when making investments will result in better performance in expectation (positive expected cost of ignoring uncertainty). Furthermore, eliminating the uncertainty would improve strategies even further, meaning that improved forecasts of future conditions are valuable ( i.e., a positive expected value of information). Also, the value of policy coordination shows the difference between a strategy developed under the incorrect assumption of no carbon cap and a strategy correctly anticipating imposition of such a cap. For the second question, game theory models are formulated and the existence of optimistic (positive) biases in market equilibria (both competitive and oligopoly markets) are proved, in that calculated social welfare and producer profits will, in expectation, exceed the values that will actually be received

  5. Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias?

    Science.gov (United States)

    Hutto, Richard L

    2016-07-01

    The most popular method used to gain an understanding of population trends or of differences in bird abundance among land condition categories is to use information derived from point counts. Unfortunately, various factors can affect one's ability to detect birds, and those factors need to be controlled or accounted for so that any difference in one's index among time periods or locations is an accurate reflection of differences in bird abundance and not differences in detectability. Avian ecologists could use appropriately sized fixed-area surveys to minimize the chance that they might be deceived by distance-based detectability bias, but the current method of choice is to use a modeling approach that allows one to account for distance-based bias by modeling the effects of distance on detectability or occupancy. I challenge the idea that modeling is the best approach to account for distance-based effects on the detectability of birds because the most important distance-based modeling assumptions can never be met. The use of a fixed-area survey method to generate an index of abundance is the simplest way to control for distance-based detectability bias and should not be universally condemned or be the basis for outright rejection in the publication process. © 2016 by the Ecological Society of America.

  6. Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data

    DEFF Research Database (Denmark)

    Greve, Douglas N; Svarer, Claus; Fisher, Patrick M

    2014-01-01

    estimates. Volume-based smoothing resulted in large bias and intersubject variance because it smears signal across tissue types. In some cases, PVC with volume smoothing paradoxically caused the estimated BPND to be less than when no PVC was used at all. When applied in the absence of PVC, cortical surface...

  7. Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    with quarterly data from 1952 to 2006. The results show that correcting the VAR parameters for small-sample bias has both quantitatively and qualitatively important e¤ects on the strategic intertemporal part of optimal portfolio choice, especially for bonds: for intermediate values of risk...

  8. Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information

    OpenAIRE

    Kayanan, Manickavasagar; Wijekoon, Pushpakanthie

    2017-01-01

    In this article, the analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multicollinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted Principal Component Regression Estimator (SRPCR), Stochastic R...

  9. Modeling Random Telegraph Noise Under Switched Bias Conditions Using Cyclostationary RTS Noise

    NARCIS (Netherlands)

    van der Wel, A.P.; Klumperink, Eric A.M.; Vandamme, L.K.J.; Nauta, Bram

    In this paper, we present measurements and simulation of random telegraph signal (RTS) noise in n-channel MOSFETs under periodic large signal gate-source excitation (switched bias conditions). This is particularly relevant to analog CMOS circuit design where large signal swings occur and where LF

  10. CPI Bias in Korea

    Directory of Open Access Journals (Sweden)

    Chul Chung

    2007-12-01

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

  11. Bayesian Model Selection in Geophysics: The evidence

    Science.gov (United States)

    Vrugt, J. A.

    2016-12-01

    Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of the model parameters. Per Bayes theorem, the posterior probability, , P(H|D), of a hypothesis, H, given the data D, is equivalent to the product of its prior probability, P(H), and likelihood, L(H|D), divided by a normalization constant, P(D). In geophysics, the hypothesis, H, often constitutes a description (parameterization) of the subsurface for some entity of interest (e.g. porosity, moisture content). The normalization constant, P(D), is not required for inference of the subsurface structure, yet of great value for model selection. Unfortunately, it is not particularly easy to estimate P(D) in practice. Here, I will introduce the various building blocks of a general purpose method which provides robust and unbiased estimates of the evidence, P(D). This method uses multi-dimensional numerical integration of the posterior (parameter) distribution. I will then illustrate this new estimator by application to three competing subsurface models (hypothesis) using GPR travel time data from the South Oyster Bacterial Transport Site, in Virginia, USA. The three subsurface models differ in their treatment of the porosity distribution and use (a) horizontal layering with fixed layer thicknesses, (b) vertical layering with fixed layer thicknesses and (c) a multi-Gaussian field. The results of the new estimator are compared against the brute force Monte Carlo method, and the Laplace-Metropolis method.

  12. Simulating publication bias

    DEFF Research Database (Denmark)

    Paldam, Martin

    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...

  13. Shiba states and zero-bias anomalies in the hybrid normal-superconductor Anderson model

    Science.gov (United States)

    Žitko, Rok; Lim, Jong Soo; López, Rosa; Aguado, Ramón

    2015-01-01

    Hybrid semiconductor-superconductor systems are interesting melting pots where various fundamental effects in condensed-matter physics coexist. For example, when a quantum dot is coupled to a superconducting electrode two very distinct phenomena, superconductivity and the Kondo effect, compete. As a result of this competition, the system undergoes a quantum phase transition when the superconducting gap Δ is of the order of the Kondo temperature TK. The underlying physics behind such transition ultimately relies on the physics of the Anderson model where the standard metallic host is replaced by a superconducting one, namely the physics of a (quantum) magnetic impurity in a superconductor. A characteristic feature of this hybrid system is the emergence of subgap bound states, the so-called Yu-Shiba-Rusinov (YSR) states, which cross zero energy across the quantum phase transition, signaling a switching of the fermion parity and spin (doublet or singlet) of the ground state. Interestingly, similar hybrid devices based on semiconducting nanowires with spin-orbit coupling may host exotic zero-energy bound states with Majorana character. Both parity crossings and Majorana bound states (MBSs) are experimentally marked by zero-bias anomalies in transport, which are detected by coupling the hybrid device with an extra normal contact. We here demonstrate theoretically that this extra contact, usually considered as a nonperturbing tunneling weak probe, leads to nontrivial effects. This conclusion is supported by numerical renormalization-group calculations of the phase diagram of an Anderson impurity coupled to both superconducting and normal-state leads. We obtain this phase diagram for an arbitrary ratio Δ/TK, which allows us to analyze relevant experimental scenarios, such as parity crossings as well as Kondo features induced by the normal lead, as this ratio changes. Spectral functions at finite temperatures and magnetic fields, which can be directly linked to

  14. High-dimensional model estimation and model selection

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.

  15. "Model age-based" and "copy when uncertain" biases in children's social learning of a novel task.

    Science.gov (United States)

    Wood, Lara A; Harrison, Rachel A; Lucas, Amanda J; McGuigan, Nicola; Burdett, Emily R R; Whiten, Andrew

    2016-10-01

    Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N=140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children's continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of model. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Starburst modeling of M82 - Test case for a biased initial mass function

    Science.gov (United States)

    Rieke, G. H.; Loken, K.; Rieke, M. J.; Tamblyn, P.

    1993-01-01

    We compute starburst models for M82, making use of recent theoretical tracks of stellar evolution. Detailed comparisons of our models and those of others demonstrate this technique to be quite reliable, with relatively little change in output parameters as a function of the selection of theoretical tracks or of estimates of the observational characteristics of the stars along these tracks. The models are matched to the observational constraints for M82 summarized by McLeod et al. (1993). The rate of star formation and time of observation were thoroughly optimized to produce the most favorable fit to the observations, but we still found that the recently proposed forms for the solar-neighborhood IMF cannot produce starbursts adequate to fit the observations of this galaxy. We then explored adjustments to the shape of the IMF to improve the fit to M82. We find (1) the shape of the IMF for high-mass stars need not be different from that observed locally; and (2) the most likely modification to the IMF in M82 is that stars with masses below a few solar masses form much less commonly than in the solar neighborhood.

  17. Selecting a model of supersymmetry breaking mediation

    International Nuclear Information System (INIS)

    AbdusSalam, S. S.; Allanach, B. C.; Dolan, M. J.; Feroz, F.; Hobson, M. P.

    2009-01-01

    We study the problem of selecting between different mechanisms of supersymmetry breaking in the minimal supersymmetric standard model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA, mGMSB, mAMSB, and moduli mediation. The results show a strong dependence on the dark matter assumption. Using the inferred cosmological relic density as an upper bound, minimal anomaly mediation is at least moderately favored over the CMSSM. Our fits also indicate that evidence for a positive sign of the μ parameter is moderate at best. We present constraints on the anomaly and gauge mediated parameter spaces and some previously unexplored aspects of the dark matter phenomenology of the moduli mediation scenario. We use sparticle searches, indirect observables and dark matter observables in the global fit and quantify robustness with respect to prior choice. We quantify how much information is contained within each constraint.

  18. Initial identification & selection bias versus the eventual confirmation of talent: evidence for the benefits of a rocky road?

    Science.gov (United States)

    McCarthy, Neil; Collins, Dave

    2014-01-01

    The relative age effect (RAE), whereby earlier birthdate children within a selection year are more commonly selected as talented, has been highlighted in the literature. As a consequence, these young athletes get into specialised training earlier and in greater numbers, leading (it is suggested) to a disproportionate opportunity for success. However, this disproportionality seems not to be manifest in senior teams. Accordingly, we examine the identification and conversion rates for academy rugby players, examining a sample of all players passing into and either graduating, or being dismissed from, a major English rugby academy. Data demonstrated a reversal of the RAE "benefit", whereby late-birth players were less likely to be selected, but more likely to achieve senior professional status. Possible reasons are explored and, on the basis of our data, we propose a psychologically based explanation of greater "growth" due to additional challenge experienced by these initially disadvantaged younger players.

  19. Growth differences and competition between Listeria monocytogenes strains determine their predominance on ham slices and lead to bias during selective enrichment with the ISO protocol.

    Science.gov (United States)

    Zilelidou, Evangelia; Manthou, Evanthia; Skandamis, Panagiotis

    2016-10-17

    Listeria monocytogenes strains are widespread in the environment where they live well mixed, often resulting in multiple strains contaminating a single food sample. The occurrence of different strains in the same food might trigger strain competition, contributing to uneven growth of strains in food and to bias during selective procedures. We tested the growth of seven L. monocytogenes strains (C5, 6179, ScottA, PL24, PL25, PL26, PL27) on ham slices and on nutrient-rich agar at 10°C, singly and in combinations. Strains were made resistant to different antibiotics for their selective enumeration. In addition, growth of single strains (axenic culture) and competition between strains in xenic cultures of two strains was evaluated in enrichment broth and on selective agar. According to ISO 11290-1:1996/Amd 1:2004 standard protocol for detection of L. monocytogenes, two enrichment steps both followed by streaking on ALOA were performed. Strain cultures were directly added in the enrichment broth or used to inoculate minced beef and sliced hams which were then mixed with enrichment broth. 180-360 colonies were used to determine the relative percentage of each strain recovered on plates per enrichment step. The data showed a significant impact of co-cultivation on the growth of six out of seven strains on ham and a bias towards certain strains during selective enrichment. Competition was manifested by: (i) cessation of growth for the outcompeted strain when the dominant strain reached stationary phase, (ii) reduction of growth rates or (iii) total suppression of growth (both on ham and in enrichment broth or ALOA). Outgrowth of strains by their competitors on ALOA resulted in limited to no recovery, with the outcompeting strain accounting for up to 100% of the total recovered colonies. The observed bias was associated with the enrichment conditions (i.e. food type added to the enrichment broth) and the strain-combination. The outcome of growth competition on food or

  20. Understanding Hydroclimatic Extremes in Changing Monsoon Climates with Daily Bias Correction of CMIP5 Regional Climate Models over South Asia

    Science.gov (United States)

    Hasan, M. A.; Islam, A. S.; Akanda, A. S. S.

    2015-12-01

    The assessment of hydroclimatic and hydrometeorological extremes in changing climates has gathered special attention in the latest IPCC 5thAssessment Report (AR5). In monsoon regions such as South Asia, hydrologic modeling (i.e., stream flow assessment, water budget analysis, etc.) needs to incorporate such extremes to simulate retrospective and future scenarios. For information of past and future climate, Regional Climate Models (RCMs) are preferred over global models due to their higher resolution and dynamic downscaling capabilities. Although the models perform well in representing the mean climate, they still possess significant biases, especially in daily hydrometeorological extremes over monsoon regions. Therefore, modification and correction of RCM results while preserving the extremes are crucial for hydrologic modeling in changing monsoon climates such as in South Asia. In this context, we generate a gridded observed product that preserve the hydroclimatic and hydrometeorological extremes for the Ganges-Brahmaputra-Meghna (GBM) basin region in South Asia. A recent approach to bias correction is also proposed for correcting regional climate data in currently available future projections. The 30 year dataset (1971-2010) is used for comparing hydroclimatic and hydrometeorological extremes with APHRODITE and ERA-Interim Reanalysis products. The assessment has revealed that the new gridded data set provides much accurate maximum rainfall intensity, number of dry days, number of wet days and number of rainy days with greater than 500mm rainfall than any other available gridded data products. Using the gridded data sets, bias correctionis applied on CMIP5 multi-model historical datasets to evaluate RCM data performance over the region, which show great improvement in regional climate data for future hydrologic modeling scenarios and analyzing impacts of climate extremes.

  1. Confirmation bias in online searches: Impacts of selective exposure before an election on political attitude strength and shifts

    NARCIS (Netherlands)

    Knobloch-Westerwick, S.; Johnson, B.K.; Westerwick, A.

    2015-01-01

    Impacts of Internet use on political information seeking and subsequent processes have been subject to much debate. A 2-session online field study presented online search results on political topics to examine selective exposure and its attitudinal impacts. Session 1 captured attitudes, including

  2. A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models.

    Science.gov (United States)

    Mayr, Andreas; Schmid, Matthias; Pfahlberg, Annette; Uter, Wolfgang; Gefeller, Olaf

    2017-06-01

    Measurement errors of medico-technical devices can be separated into systematic bias and random error. We propose a new method to address both simultaneously via generalized additive models for location, scale and shape (GAMLSS) in combination with permutation tests. More precisely, we extend a recently proposed boosting algorithm for GAMLSS to provide a test procedure to analyse potential device effects on the measurements. We carried out a large-scale simulation study to provide empirical evidence that our method is able to identify possible sources of systematic bias as well as random error under different conditions. Finally, we apply our approach to compare measurements of skin pigmentation from two different devices in an epidemiological study.

  3. Optimization of an extraction protocol for organic matter from soils and sediments using high resolution mass spectrometry: selectivity and biases

    Science.gov (United States)

    Chu, R. K.; Tfaily, M. M.; Tolic, N.; Kyle, J. E.; Robinson, E. R.; Hess, N. J.; Paša-Tolić, L.

    2015-12-01

    Soil organic matter (SOM) is a complex mixture of above and belowground plant litter and microbial residues, and is a key reservoir for carbon (C) and nutrient biogeochemical cycling in different ecosystems. A limited understanding of the molecular composition of SOM prohibits the ability to routinely decipher chemical processes within soil and predict how terrestrial C fluxes will response to changing climatic conditions. Here, we present that the choice of solvent can be used to selectively extract different compositional fractions from SOM to either target a specific class of compounds or gain a better understanding of the entire composition of the soil sample using 12T Fourier transform ion cyclotron resonance mass spectrometry. Specifically, we found that hexane and chloroform were selective for lipid-like compounds with very low O:C ratios; water was selective for carbohydrates with high O:C ratios; acetonitrile preferentially extracts lignin, condensed structures, and tannin polyphenolic compounds with O:C > 0.5; methanol has higher selectivity towards lignin and lipid compounds characterized with relatively low O:C soil for a broader range of chemically diverse soil types. Since each solvent extracts a selective group of compounds, using a suite of solvents with varying polarity for analysis results in more comprehensive representation of the diversity of organic molecules present in soil and a better representation of the whole spectrum of available substrates for microorganisms. Moreover, we have developed a sequential extraction protocol that permits sampling diverse classes of organic compounds while minimizing ionization competition during ESI while increasing sample throughput and decreasing sample volume. This allowed us to hypothesize about possible chemical reactions relating classes of organic molecules that reflect abiotic and biotic processes impacting SOM composition.

  4. Selective enrichment media bias the types of Salmonella enterica strains isolated from mixed strain cultures and complex enrichment broths.

    Science.gov (United States)

    Gorski, Lisa

    2012-01-01

    For foodborne outbreak investigations it can be difficult to isolate the relevant strain from food and/or environmental sources. If the sample is contaminated by more than one strain of the pathogen the relevant strain might be missed. In this study mixed cultures of Salmonella enterica were grown in one set of standard enrichment media to see if culture bias patterns emerged. Nineteen strains representing four serogroups and ten serotypes were compared in four-strain mixtures in Salmonella-only and in cattle fecal culture enrichment backgrounds using Salmonella enrichment media. One or more strain(s) emerged as dominant in each mixture. No serotype was most fit, but strains of serogroups C2 and E were more likely to dominate enrichment culture mixtures than strains of serogroups B or C1. Different versions of Rappaport-Vassiliadis (RV) medium gave different patterns of strain dominance in both Salmonella-only and fecal enrichment culture backgrounds. The fittest strains belonged to serogroups C1, C2, and E, and included strains of S. Infantis, S. Thompson S. Newport, S. 6,8:d:-, and S. Give. Strains of serogroup B, which included serotypes often seen in outbreaks such as S. Typhimurium, S. Saintpaul, and S. Schwarzengrund were less likely to emerge as dominant strains in the mixtures when using standard RV as part of the enrichment. Using a more nutrient-rich version of RV as part of the protocol led to a different pattern of strains emerging, however some were still present in very low numbers in the resulting population. These results indicate that outbreak investigations of food and/or other environmental samples should include multiple enrichment protocols to ensure isolation of target strains of Salmonella.

  5. Selective prevention of combat-related post-traumatic stress disorder using attention bias modification training: a randomized controlled trial.

    Science.gov (United States)

    Wald, I; Fruchter, E; Ginat, K; Stolin, E; Dagan, D; Bliese, P D; Quartana, P J; Sipos, M L; Pine, D S; Bar-Haim, Y

    2016-09-01

    Efficacy of pre-trauma prevention for post-traumatic stress disorder (PTSD) has not yet been established in a randomized controlled trial. Attention bias modification training (ABMT), a computerized intervention, is thought to mitigate stress-related symptoms by targeting disruptions in threat monitoring. We examined the efficacy of ABMT delivered before combat in mitigating risk for PTSD following combat. We conducted a double-blind, four-arm randomized controlled trial of 719 infantry soldiers to compare the efficacy of eight sessions of ABMT (n = 179), four sessions of ABMT (n = 184), four sessions of attention control training (ACT; n = 180), or no-training control (n = 176). Outcome symptoms were measured at baseline, 6-month follow-up, 10 days following combat exposure, and 4 months following combat. Primary outcome was PTSD prevalence 4 months post-combat determined in a clinical interview using the Clinician-Administered PTSD Scale. Secondary outcomes were self-reported PTSD and depression symptoms, collected at all four assessments. PTSD prevalence 4 months post-combat was 7.8% in the no-training control group, 6.7% with eight-session ABMT, 2.6% with four-session ABMT, and 5% with ACT. Four sessions of ABMT reduced risk for PTSD relative to the no-training condition (odds ratio 3.13, 95% confidence interval 1.01-9.22, p < 0.05, number needed to treat = 19.2). No other between-group differences were found. The results were consistent across a variety of analytic techniques and data imputation approaches. Four sessions of ABMT, delivered prior to combat deployment, mitigated PTSD risk following combat exposure. Given its low cost and high scalability potential, and observed number needed to treat, research into larger-scale applications is warranted. The ClinicalTrials.gov identifier is NCT01723215.

  6. Hidden Markov Model for Stock Selection

    Directory of Open Access Journals (Sweden)

    Nguyet Nguyen

    2015-10-01

    Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.

  7. Impact of model resolution on simulating the water vapor transport through the central Himalayas: implication for models' wet bias over the Tibetan Plateau

    Science.gov (United States)

    Lin, Changgui; Chen, Deliang; Yang, Kun; Ou, Tinghai

    2018-01-01

    Current climate models commonly overestimate precipitation over the Tibetan Plateau (TP), which limits our understanding of past and future water balance in the region. Identifying sources of such models' wet bias is therefore crucial. The Himalayas is considered a major pathway of water vapor transport (WVT) towards the TP. Their steep terrain, together with associated small-scale processes, cannot be resolved by coarse-resolution models, which may result in excessive WVT towards the TP. This paper, therefore, investigated the resolution dependency of simulated WVT through the central Himalayas and its further impact on precipitation bias over the TP. According to a summer monsoon season of simulations conducted using the weather research forecasting (WRF) model with resolutions of 30, 10, and 2 km, the study found that finer resolutions (especially 2 km) diminish the positive precipitation bias over the TP. The higher-resolution simulations produce more precipitation over the southern Himalayan slopes and weaker WVT towards the TP, explaining the reduced wet bias. The decreased WVT is reflected mostly in the weakened wind speed, which is due to the fact that the high resolution can improve resolving orographic drag over a complex terrain and other processes associated with heterogeneous surface forcing. A significant difference was particularly found when the model resolution is changed from 30 to 10 km, suggesting that a resolution of approximately 10 km represents a good compromise between a more spatially detailed simulation of WVT and computational cost for a domain covering the whole TP.

  8. Psyche Mission: Scientific Models and Instrument Selection

    Science.gov (United States)

    Polanskey, C. A.; Elkins-Tanton, L. T.; Bell, J. F., III; Lawrence, D. J.; Marchi, S.; Park, R. S.; Russell, C. T.; Weiss, B. P.

    2017-12-01

    NASA has chosen to explore (16) Psyche with their 14th Discovery-class mission. Psyche is a 226-km diameter metallic asteroid hypothesized to be the exposed core of a planetesimal that was stripped of its rocky mantle by multiple hit and run collisions in the early solar system. The spacecraft launch is planned for 2022 with arrival at the asteroid in 2026 for 21 months of operations. The Psyche investigation has five primary scientific objectives: A. Determine whether Psyche is a core, or if it is unmelted material. B. Determine the relative ages of regions of Psyche's surface. C. Determine whether small metal bodies incorporate the same light elements as are expected in the Earth's high-pressure core. D. Determine whether Psyche was formed under conditions more oxidizing or more reducing than Earth's core. E. Characterize Psyche's topography. The mission's task was to select the appropriate instruments to meet these objectives. However, exploring a metal world, rather than one made of ice, rock, or gas, requires development of new scientific models for Psyche to support the selection of the appropriate instruments for the payload. If Psyche is indeed a planetary core, we expect that it should have a detectable magnetic field. However, the strength of the magnetic field can vary by orders of magnitude depending on the formational history of Psyche. The implications of both the extreme low-end and the high-end predictions impact the magnetometer and mission design. For the imaging experiment, what can the team expect for the morphology of a heavily impacted metal body? Efforts are underway to further investigate the differences in crater morphology between high velocity impacts into metal and rock to be prepared to interpret the images of Psyche when they are returned. Finally, elemental composition measurements at Psyche using nuclear spectroscopy encompass a new and unexplored phase space of gamma-ray and neutron measurements. We will present some end

  9. Making Steppingstones out of Stumbling Blocks: A New Bayesian Model Evidence Estimator with Application to Groundwater Model Selection

    Science.gov (United States)

    Ye, M.; Elshall, A. S.; Tang, G.; Samani, S.

    2016-12-01

    Bayesian Model Evidence (BME) is the measure of the average fit of the model to data given all the parameter values that the model can take. By accounting for the trade-off between the model ability to reproduce the observation data and model complexity, BME estimates of candidate models are employed to calculate model weights, which are used for model selection and model averaging. This study shows that accurate estimation of the BME is important for penalizing models with more complexity. To improve the accuracy of BME estimation, we resort to Monte Carlo numerical estimators over semi-analytical solutions (such as Laplace approximations, BIC, KIC and other). This study examines prominent numerical estimators of BME that are the thermodynamic integration (TI), and the importance sampling methods of arithmetic mean (AM), harmonic mean (HM), and steppingstone sampling (SS). AM estimator (based on prior sampling) and HM estimator (based on posterior sampling) are straightforward to implement, yet they lead to under and over estimation, respectively. TI and SS improve beyond this by means of sampling multiple intermediate distributions that links the prior and the posterior, using Markov Chain Monte Carlo (MCMC). TI and SS are theoretically unbiased estimators that are mathematically rigorous. Yet a theoretically unbiased estimator could have large bias in practice arising from numerical implementation, because MCMC sampling errors of certain intermediate distributions can introduce bias. We propose an SS variant, namely the multiple one-steppingstone sampling (MOSS), which turns these intermediate stumbling "blocks" of SS into steppingstones toward BME estimation. Thus, MOSS is less sensitive to MCMC sampling errors. We evaluate these estimators using a problem of groundwater transport model selection. The modeling results show that SS and MOSS estimators gave the most accurate results. In addition, the results show that the magnitude of the estimation error is a

  10. Beyond Rational Decision-Making: Modelling the Influence of Cognitive Biases on the Dynamics of Vaccination Coverage.

    Science.gov (United States)

    Voinson, Marina; Billiard, Sylvain; Alvergne, Alexandra

    2015-01-01

    Theoretical studies predict that it is not possible to eradicate a disease under voluntary vaccination because of the emergence of non-vaccinating "free-riders" when vaccination coverage increases. A central tenet of this approach is that human behaviour follows an economic model of rational choice. Yet, empirical studies reveal that vaccination decisions do not necessarily maximize individual self-interest. Here we investigate the dynamics of vaccination coverage using an approach that dispenses with payoff maximization and assumes that risk perception results from the interaction between epidemiology and cognitive biases. We consider a behaviour-incidence model in which individuals perceive actual epidemiological risks as a function of their opinion of vaccination. As a result of confirmation bias, sceptical individuals (negative opinion) overestimate infection cost while pro-vaccines individuals (positive opinion) overestimate vaccination cost. We considered a feedback between individuals and their environment as individuals could change their opinion, and thus the way they perceive risks, as a function of both the epidemiology and the most common opinion in the population. For all parameter values investigated, the infection is never eradicated under voluntary vaccination. For moderately contagious diseases, oscillations in vaccination coverage emerge because individuals process epidemiological information differently depending on their opinion. Conformism does not generate oscillations but slows down the cultural response to epidemiological change. Failure to eradicate vaccine preventable disease emerges from the model because of cognitive biases that maintain heterogeneity in how people perceive risks. Thus, assumptions of economic rationality and payoff maximization are not mandatory for predicting commonly observed dynamics of vaccination coverage. This model shows that alternative notions of rationality, such as that of ecological rationality whereby

  11. Beyond Rational Decision-Making: Modelling the Influence of Cognitive Biases on the Dynamics of Vaccination Coverage.

    Directory of Open Access Journals (Sweden)

    Marina Voinson

    Full Text Available Theoretical studies predict that it is not possible to eradicate a disease under voluntary vaccination because of the emergence of non-vaccinating "free-riders" when vaccination coverage increases. A central tenet of this approach is that human behaviour follows an economic model of rational choice. Yet, empirical studies reveal that vaccination decisions do not necessarily maximize individual self-interest. Here we investigate the dynamics of vaccination coverage using an approach that dispenses with payoff maximization and assumes that risk perception results from the interaction between epidemiology and cognitive biases.We consider a behaviour-incidence model in which individuals perceive actual epidemiological risks as a function of their opinion of vaccination. As a result of confirmation bias, sceptical individuals (negative opinion overestimate infection cost while pro-vaccines individuals (positive opinion overestimate vaccination cost. We considered a feedback between individuals and their environment as individuals could change their opinion, and thus the way they perceive risks, as a function of both the epidemiology and the most common opinion in the population.For all parameter values investigated, the infection is never eradicated under voluntary vaccination. For moderately contagious diseases, oscillations in vaccination coverage emerge because individuals process epidemiological information differently depending on their opinion. Conformism does not generate oscillations but slows down the cultural response to epidemiological change.Failure to eradicate vaccine preventable disease emerges from the model because of cognitive biases that maintain heterogeneity in how people perceive risks. Thus, assumptions of economic rationality and payoff maximization are not mandatory for predicting commonly observed dynamics of vaccination coverage. This model shows that alternative notions of rationality, such as that of ecological

  12. Beyond Rational Decision-Making: Modelling the Influence of Cognitive Biases on the Dynamics of Vaccination Coverage

    Science.gov (United States)

    Voinson, Marina; Billiard, Sylvain; Alvergne, Alexandra

    2015-01-01

    Background Theoretical studies predict that it is not possible to eradicate a disease under voluntary vaccination because of the emergence of non-vaccinating “free-riders” when vaccination coverage increases. A central tenet of this approach is that human behaviour follows an economic model of rational choice. Yet, empirical studies reveal that vaccination decisions do not necessarily maximize individual self-interest. Here we investigate the dynamics of vaccination coverage using an approach that dispenses with payoff maximization and assumes that risk perception results from the interaction between epidemiology and cognitive biases. Methods We consider a behaviour-incidence model in which individuals perceive actual epidemiological risks as a function of their opinion of vaccination. As a result of confirmation bias, sceptical individuals (negative opinion) overestimate infection cost while pro-vaccines individuals (positive opinion) overestimate vaccination cost. We considered a feedback between individuals and their environment as individuals could change their opinion, and thus the way they perceive risks, as a function of both the epidemiology and the most common opinion in the population. Results For all parameter values investigated, the infection is never eradicated under voluntary vaccination. For moderately contagious diseases, oscillations in vaccination coverage emerge because individuals process epidemiological information differently depending on their opinion. Conformism does not generate oscillations but slows down the cultural response to epidemiological change. Conclusion Failure to eradicate vaccine preventable disease emerges from the model because of cognitive biases that maintain heterogeneity in how people perceive risks. Thus, assumptions of economic rationality and payoff maximization are not mandatory for predicting commonly observed dynamics of vaccination coverage. This model shows that alternative notions of rationality, such as

  13. A DIRECT MEASUREMENT OF THE LINEAR BIAS OF MID-INFRARED-SELECTED QUASARS AT z ≈ 1 USING COSMIC MICROWAVE BACKGROUND LENSING

    Energy Technology Data Exchange (ETDEWEB)

    Geach, J. E. [Centre for Astrophysics Research, Science and Technology Research Institute, University of Hertfordshire, Hatfield, AL10 9AB (United Kingdom); Hickox, R. C.; Hainline, K. N. [Department of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755 (United States); Bleem, L. E.; Benson, B. A.; Bhattacharya, S.; Carlstrom, J. E.; Chang, C. L.; Crawford, T. M.; Crites, A. T. [Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637 (United States); Brodwin, M. [Department of Physics and Astronomy, University of Missouri, Kansas City, MO 64110 (United States); Holder, G. P.; De Haan, T.; Dobbs, M. A.; Dudley, J. [Department of Physics, McGill University, Montreal, Quebec H3A 2T8 (Canada); Aird, K. A. [University of Chicago, Chicago, IL 60637 (United States); Cho, H.-M. [NIST Quantum Devices Group, Boulder, CO 80305 (United States); George, E. M.; Holzapfel, W. L. [Department of Physics, University of California, Berkeley, CA 94720 (United States); Halverson, N. W., E-mail: j.geach@herts.ac.uk [Department of Astrophysical and Planetary Sciences and Department of Physics, University of Colorado, Boulder, CO 80309 (United States); and others

    2013-10-20

    We measure the cross-power spectrum of the projected mass density as traced by the convergence of the cosmic microwave background lensing field from the South Pole Telescope (SPT) and a sample of Type 1 and 2 (unobscured and obscured) quasars at (z) ∼ 1 selected with the Wide-field Infrared Survey Explorer, over 2500 deg{sup 2}. The cross-power spectrum is detected at ≈7σ, and we measure a linear bias b = 1.61 ± 0.22, consistent with clustering analyses. Using an independent lensing map, derived from Planck observations, to measure the cross-spectrum, we find excellent agreement with the SPT analysis. The bias of the combined sample of Type 1 and 2 quasars determined in this work is similar to that previously determined for Type 1 quasars alone; we conclude that obscured and unobscured quasars trace the matter field in a similar way. This result has implications for our understanding of quasar unification and evolution schemes.

  14. 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...... 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........ 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...

  15. Modeling of exchange bias in the antiferromagnetic (core)/ferromagnetic (shell) nanoparticles with specialized shapes

    International Nuclear Information System (INIS)

    Hu Yong; Liu Yan; Du An

    2011-01-01

    Zero-field-cooled (ZFC) and field-cooled (FC) hysteresis loops of egg- and ellipsoid-shaped nanoparticles with inverted ferromagnetic (FM)-antiferromagnetic (AFM) core-shell morphologies are simulated using a modified Monte Carlo method, which takes into account both the thermal fluctuations and energy barriers during the rotation of spin. Pronounced exchange bias (EB) fields and reduced coercivities are obtained in the FC hysteresis loops. The analysis of the microscopic spin configurations allows us to conclude that the magnetization reversal occurs by means of the nucleation process during both the ZFC and FC hysteresis branches. The nucleation takes place in the form of 'sparks' resulting from the energy competition and the morphology of the nanoparticle. The appearance of EB in the FC hysteresis loops is only dependent on that the movements of 'sparks' driven by magnetic field at both branches of hysteresis loops are not along the same axis, which is independent of the strength of AFM anisotropy. The tilt of 'spark' movement with respect to the symmetric axis implies the existence of additional unidirectional anisotropy at the AFM/FM interfaces as a consequence of the surplus magnetization in the AFM core, which is the commonly accepted origin of EB. Our simulations allow us to clarify the microscopic mechanisms of the observed EB behavior, not accessible in experiments. - Highlights: → A modified Monte Carlo method considers thermal fluctuations and energy barriers. → Egg and ellipsoid nanoparticles with inverted core-shell morphology are studied. → Pronounced exchange bias fields and reduced coercivities may be detected. → 'Sparks' representing nucleation sites due to energy competition are observed. → 'Sparks' can reflect or check directly and vividly the origin of exchange bias.

  16. Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

    Science.gov (United States)

    Sidik, S. M.

    1975-01-01

    Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

  17. The tenure gap in electoral participation: instrumental motivation or selection bias? Comparing homeowners and tenants across four housing regimes.

    Science.gov (United States)

    André, Stéfanie; Dewilde, Caroline; Luijkx, Ruud

    2017-06-01

    Integrating housing tenure in Instrumental Motivation Theory predicts a tenure gap in electoral participation, as homeowners would be more motivated to vote compared with tenants. The empirical question is whether this effect is causal or rather due to selection into different housing tenures. This question is tackled using coarsened exact matching (CEM) on data for 19 countries, allowing us to better control for endogeneity. Even then, homeowners are found to vote more often than tenants. This association is stronger in countries characterized by a strong pro-homeownership ideology and/or where the financialization of housing markets turned houses into assets.

  18. Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

    Science.gov (United States)

    Cohen, Charlie; Robertson, Franklin; Molod, Andrea

    2014-01-01

    The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of

  19. Stratified sampling design and loss to follow-up in survival models: evaluation of efficiency and bias

    Directory of Open Access Journals (Sweden)

    Carvalho Marilia S

    2011-06-01

    Full Text Available Abstract Background Longitudinal studies often employ complex sample designs to optimize sample size, over-representing population groups of interest. The effect of sample design on parameter estimates is quite often ignored, particularly when fitting survival models. Another major problem in long-term cohort studies is the potential bias due to loss to follow-up. Methods In this paper we simulated a dataset with approximately 50,000 individuals as the target population and 15,000 participants to be followed up for 40 years, both based on real cohort studies of cardiovascular diseases. Two sample strategies - simple random (our golden standard and Stratified by professional group, with non-proportional allocation - and two loss to follow-up scenarios - non-informative censoring and losses related to the professional group - were analyzed. Results Two modeling approaches were evaluated: weighted and non-weighted fit. Our results indicate that under the correctly specified model, ignoring the sample weights does not affect the results. However, the model ignoring the interaction of sample strata with the variable of interest and the crude estimates were highly biased. Conclusions In epidemiological studies misspecification should always be considered, as different sources of variability, related to the individuals and not captured by the covariates, are always present. Therefore, allowance must be made for the possibility of unknown confounders and interactions with the main variable of interest in our data. It is strongly recommended always to correct by sample weights.

  20. An Analysis of the Effectiveness of Supplemental Instruction: The Problem of Selection Bias and Limited Dependent Variables

    Science.gov (United States)

    Bowles, Tyler J.; Jones, Jason

    2004-01-01

    Single equation regression models have been used rather extensively to test the effectiveness of Supplemental Instruction (SI). This approach, however, fails to account for the possibility that SI attendance and the outcome of SI attendance are jointly determined endogenous variables. Moreover, the standard approach fails to account for the fact…

  1. A new Russell model for selecting suppliers

    NARCIS (Netherlands)

    Azadi, Majid; Shabani, Amir; Farzipoor Saen, Reza

    2014-01-01

    Recently, supply chain management (SCM) has been considered by many researchers. Supplier evaluation and selection plays a significant role in establishing an effective SCM. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In some situations, to

  2. Selective experimental review of the Standard Model

    International Nuclear Information System (INIS)

    Bloom, E.D.

    1985-02-01

    Before disussing experimental comparisons with the Standard Model, (S-M) it is probably wise to define more completely what is commonly meant by this popular term. This model is a gauge theory of SU(3)/sub f/ x SU(2)/sub L/ x U(1) with 18 parameters. The parameters are α/sub s/, α/sub qed/, theta/sub W/, M/sub W/ (M/sub Z/ = M/sub W//cos theta/sub W/, and thus is not an independent parameter), M/sub Higgs/; the lepton masses, M/sub e/, Mμ, M/sub r/; the quark masses, M/sub d/, M/sub s/, M/sub b/, and M/sub u/, M/sub c/, M/sub t/; and finally, the quark mixing angles, theta 1 , theta 2 , theta 3 , and the CP violating phase delta. The latter four parameters appear in the quark mixing matrix for the Kobayashi-Maskawa and Maiani forms. Clearly, the present S-M covers an enormous range of physics topics, and the author can only lightly cover a few such topics in this report. The measurement of R/sub hadron/ is fundamental as a test of the running coupling constant α/sub s/ in QCD. The author will discuss a selection of recent precision measurements of R/sub hadron/, as well as some other techniques for measuring α/sub s/. QCD also requires the self interaction of gluons. The search for the three gluon vertex may be practically realized in the clear identification of gluonic mesons. The author will present a limited review of recent progress in the attempt to untangle such mesons from the plethora q anti q states of the same quantum numbers which exist in the same mass range. The electroweak interactions provide some of the strongest evidence supporting the S-M that exists. Given the recent progress in this subfield, and particularly with the discovery of the W and Z bosons at CERN, many recent reviews obviate the need for further discussion in this report. In attempting to validate a theory, one frequently searches for new phenomena which would clearly invalidate it. 49 references, 28 figures

  3. What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?

    Energy Technology Data Exchange (ETDEWEB)

    Liu, M. L.; Rajagopalan, K.; Chung, S. H.; Jiang, X.; Harrison, J. H.; Nergui, T.; Guenther, Alex B.; Miller, C.; Reyes, J.; Tague, C. L.; Choate, J. S.; Salathe, E.; Stockle, Claudio O.; Adam, J. C.

    2014-05-16

    Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables(evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These im20 pact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However

  4. 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

    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

  5. 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.

  6. The Protective Effect of Low-Dose Aspirin against Colorectal Cancer Is Unlikely Explained by Selection Bias: Results from Three Different Study Designs in Clinical Practice.

    Directory of Open Access Journals (Sweden)

    Lucía Cea Soriano

    Full Text Available We conducted three differently designed nested case-control studies to evaluate whether the protective effect of low-dose aspirin against colorectal cancer (CRC is explained by selection bias.Using a large validated UK primary care database, we followed different cohorts of patients, who varied in their demographic and clinical characteristics, to identify first ever cases of CRC. In Studies 1 and 2, two cohorts were followed, i new users of low-dose aspirin at start of follow-up (N = 170,336 in Study 1, N = 171,527 in Study 2 and either ii non-users of low-dose aspirin (Study 1, N = 170,336 or new users of paracetamol (Study 2, N = 149,597 at start of follow-up. In Study 3 a single cohort of individuals näive to low-dose aspirin at the start of observation was followed. Controls were selected using incidence sampling and logistic regression used to obtain an unbiased estimate of the incidence rate ratio (RR with 95% confidence intervals (CIs. Low-dose aspirin exposure was analyzed 'as-treated' before the index date (CRC date for cases, random date for controls.In the three studies, median (maximum follow-up was 5.1 (12, 5.8 (12 and 7.5 (13 years, respectively. 3033 incident CRC cases were identified in Study 1, 3174 in Study 2, and 12,333 in Study 3. Current use of low-dose aspirin was associated with a significantly reduced risk of 34%, 29% and 31% in the three studies, respectively; corresponding RRs (95% CIs were 0.66 (0.60-0.73, 0.71 (0.63-0.80 and 0.69 (0.64-0.74. In each study, significantly reduced risks of CRC were seen when low-dose aspirin was used for primary or secondary cardiovascular disease prevention, in both sexes, and across all age groups evaluated.Low-dose aspirin is associated with a significantly reduced risk of CRC. The consistency of our findings across different studies makes selection bias an unlikely explanation.

  7. 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.

  8. A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

    Science.gov (United States)

    Thomas, D.L.; Johnson, D.; Griffith, B.

    2006-01-01

    Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a

  9. Assessing the sensitivity of the hydroxyl radical to model biases in composition and temperature using a single-column photochemical model for Lauder, New Zealand

    Science.gov (United States)

    López-Comí, Laura; Morgenstern, Olaf; Zeng, Guang; Masters, Sarah L.; Querel, Richard R.; Nedoluha, Gerald E.

    2016-11-01

    We assess the major factors contributing to local biases in the hydroxyl radical (OH) as simulated by a global chemistry-climate model, using a single-column photochemical model (SCM) analysis. The SCM has been constructed to represent atmospheric chemistry at Lauder, New Zealand, which is representative of the background atmosphere of the Southern Hemisphere (SH) mid-latitudes. We use long-term observations of variables essential to tropospheric OH chemistry, i.e. ozone (O3), water vapour (H2O), methane (CH4), carbon monoxide (CO), and temperature, and assess how using these measurements affect OH calculated in the SCM, relative to a reference simulation only using modelled fields. The analysis spans 1994 to 2010. Results show that OH responds approximately linearly to correcting biases in O3, H2O, CO, CH4, and temperature. The biggest impact on OH is due to correcting an overestimation by approximately 20 to 60 % of H2O, using radiosonde observations. Correcting this moist bias leads to a reduction of OH by around 5 to 35 %. This is followed by correcting predominantly overestimated O3. In the troposphere, the model biases are mostly in the range of -10 to 30 %. The impact of changing O3 on OH is due to two pathways; the OH responses to both are of similar magnitude but different seasonality: correcting in situ tropospheric ozone leads to changes in OH in the range -14 to 4 %, whereas correcting the photolysis rate of O3 in accordance with overhead column ozone changes leads to increases of OH of 8 to 16 %. The OH sensitivities to correcting CH4, CO, and temperature biases are all minor effects. The work demonstrates the feasibility of quantitatively assessing OH sensitivity to biases in longer-lived species, which can help explain differences in simulated OH between global chemistry models and relative to observations. In addition to clear-sky simulations, we have performed idealized sensitivity simulations to assess the impact of clouds (ice and liquid

  10. 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

  11. 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.

  12. A two-step ionospheric modeling algorithm considering the impact of GLONASS pseudo-range inter-channel biases

    Science.gov (United States)

    Zhang, Rui; Yao, Yi-bin; Hu, Yue-ming; Song, Wei-wei

    2017-12-01

    The Global Navigation Satellite System presents a plausible and cost-effective way of computing the total electron content (TEC). But TEC estimated value could be seriously affected by the differential code biases (DCB) of frequency-dependent satellites and receivers. Unlike GPS and other satellite systems, GLONASS adopts a frequency-division multiplexing access mode to distinguish different satellites. This strategy leads to different wavelengths and inter-frequency biases (IFBs) for both pseudo-range and carrier phase observations, whose impacts are rarely considered in ionospheric modeling. We obtained observations from four groups of co-stations to analyze the characteristics of the GLONASS receiver P1P2 pseudo-range IFB with a double-difference method. The results showed that the GLONASS P1P2 pseudo-range IFB remained stable for a period of time and could catch up to several meters, which cannot be absorbed by the receiver DCB during ionospheric modeling. Given the characteristics of the GLONASS P1P2 pseudo-range IFB, we proposed a two-step ionosphere modeling method with the priori IFB information. The experimental analysis showed that the new algorithm can effectively eliminate the adverse effects on ionospheric model and hardware delay parameters estimation in different space environments. During high solar activity period, compared to the traditional GPS + GLONASS modeling algorithm, the absolute average deviation of TEC decreased from 2.17 to 2.07 TECu (TEC unit); simultaneously, the average RMS of GPS satellite DCB decreased from 0.225 to 0.219 ns, and the average deviation of GLONASS satellite DCB decreased from 0.253 to 0.113 ns with a great improvement in over 55%.

  13. Uncertainty associated with selected environmental transport models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-11-01

    A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation

  14. Quality Quandaries- Time Series Model Selection and Parsimony

    DEFF Research Database (Denmark)

    Bisgaard, Søren; Kulahci, Murat

    2009-01-01

    Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....

  15. Journal bias or author bias?

    Science.gov (United States)

    Harris, Ian

    2016-01-01

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

  16. Exploring the relation between masculinity and mental illness stigma using the stereotype content model and BIAS map.

    Science.gov (United States)

    Boysen, Guy A

    2017-01-01

    The current research explored the association of masculinity and stigma toward mental illness using theoretical predictions stemming from the stereotype content model and BIAS map. Two correlational studies (Ns = 245, 163) measured stereotypes, emotions, and behavioral intentions in relation to masculine, feminine, and gender-neutral disorders. Participants perceived masculine disorders as lacking personal warmth and competence. Masculine disorders also elicited more negative emotions and behavioral intentions. Two experimental studies (Ns = 161, 431) manipulated personal warmth, sex, and type of disorder in descriptions of people with mental illness. Low warmth and stereotypically masculine disorders consistently elicited negative emotions and behavioral intentions, but sex had limited effects. Overall, the results supported the theoretical models and illustrated the importance of warmth and symptomatic behavior in explaining the masculinity-stigma relation.

  17. A Mesoscopic Analytical Model to Predict the Onset of Wrinkling in Plain Woven Preforms under Bias Extension Shear Deformation

    Directory of Open Access Journals (Sweden)

    Abbas Hosseini

    2017-10-01

    Full Text Available A mesoscopic analytical model of wrinkling of Plain-Woven Composite Preforms (PWCPs under the bias extension test is presented, based on a new instability analysis. The analysis is aimed to facilitate a better understanding of the nature of wrinkle formation in woven fabrics caused by large in-plane shear, while it accounts for the effect of fabric and process parameters on the onset of wrinkling. To this end, the mechanism of wrinkle formation in PWCPs in mesoscale is simplified and an equivalent structure composed of bars and different types of springs is proposed, mimicking the behavior of a representative PWCP element at the post-locking state. The parameters of this equivalent structure are derived based on geometric and mechanical characteristics of the PWCP. The principle of minimum total potential energy is employed to formluate the model, and experimental validation is carried out to reveal the effectiveness of the derived wrinkling prediction equation.

  18. Fundamentals of bias temperature instability in MOS transistors characterization methods, process and materials impact, DC and AC modeling

    CERN Document Server

    2016-01-01

    This book aims to cover different aspects of Bias Temperature Instability (BTI). BTI remains as an important reliability concern for CMOS transistors and circuits. Development of BTI resilient technology relies on utilizing artefact-free stress and measurement methods and suitable physics-based models for accurate determination of degradation at end-of-life, and understanding the gate insulator process impact on BTI. This book discusses different ultra-fast characterization techniques for recovery artefact free BTI measurements. It also covers different direct measurements techniques to access pre-existing and newly generated gate insulator traps responsible for BTI. The book provides a consistent physical framework for NBTI and PBTI respectively for p- and n- channel MOSFETs, consisting of trap generation and trapping. A physics-based compact model is presented to estimate measured BTI degradation in planar Si MOSFETs having differently processed SiON and HKMG gate insulators, in planar SiGe MOSFETs and also...

  19. The Biasing Effects of Unmodeled ARMA Time Series Processes on Latent Growth Curve Model Estimates

    Science.gov (United States)

    Sivo, Stephen; Fan, Xitao; Witta, Lea

    2005-01-01

    The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify…

  20. The Role of Scale and Model Bias in ADAPT's Photospheric Eatimation

    Energy Technology Data Exchange (ETDEWEB)

    Godinez Vazquez, Humberto C. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hickmann, Kyle Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Arge, Charles Nicholas [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Henney, Carl [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-05-20

    The Air Force Assimilative Photospheric flux Transport model (ADAPT), is a magnetic flux propagation based on Worden-Harvey (WH) model. ADAPT would be used to provide a global photospheric map of the Earth. A data assimilation method based on the Ensemble Kalman Filter (EnKF), a method of Monte Carlo approximation tied with Kalman filtering, is used in calculating the ADAPT models.

  1. A 'putty-practically-clay' vintage model with R and D driven biases in energy-saving technical change

    International Nuclear Information System (INIS)

    Van Zon, A.; Lontzek, T.

    2005-01-01

    This paper deals with the problem of tackling the adverse effect of output growth on environmental quality. For this purpose we use an intermediate sector that builds 'putty-practically-clay' capital consisting of an energy-raw capital amalgam used for final goods production. The putty-practically-clay model is a strongly simplified version of a full putty-clay model, that mimics all the relevant behaviour of a full puttyclay model, but that does not entail the administrative hassle of a full putty-clay vintage model. In addition to this, we introduce an R and D sector that develops renewable- and conventional energy-based technologies. The allocation of R and D activities over these two uses of R and D gives rise to an induced bias in technical change. In the context of our model, this implies that technological progress is primarily driven by the desire to counteract the upward pressure on production cost implied by a continuing price increase of conventional energy resources. Hotelling's rule suggests that this price rise is unavoidable in the face of the ongoing depletion of conventional energy reserves. By means of some illustrative model simulations we study the effects of energy policy on the dynamics of the model for alternative policy options aimed at achieving GHG emission reductions. We identify the conditions under which energy policy might partly backfire and present some non-standard policy implications

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

    Science.gov (United States)

    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 (

  3. Development and Online Operation of Minimum Bias Triggers in ATLAS

    CERN Document Server

    Martin, T A D

    2010-01-01

    Since the restart of the LHC in November 2009, ATLAS has recorded inelastic $p - p$ collisions to perform first measurements on charged particle multiplicities. These measurements will help to constrain various phenomenological models describing soft parton interactions. Understanding trigger efficiencies for different event types is therefore crucial to minimize any possible bias in the event selection. ATLAS uses two main minimum bias triggers, utilizing complementary detector components and trigger levels. A hardware based first trigger level situated in the forward regions of $2.1 < |eta| < 3.8$ has been proven to select $p - p$ collisions with high efficiency. A complementary Inner Detector based minimum bias trigger uses a random seed on filled bunches and central tracking detectors for 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 wi...

  4. Communication: Introducing prescribed biases in out-of-equilibrium Markov models

    Science.gov (United States)

    Dixit, Purushottam D.

    2018-03-01

    Markov models are often used in modeling complex out-of-equilibrium chemical and biochemical systems. However, many times their predictions do not agree with experiments. We need a systematic framework to update existing Markov models to make them consistent with constraints that are derived from experiments. Here, we present a framework based on the principle of maximum relative path entropy (minimum Kullback-Leibler divergence) to update Markov models using stationary state and dynamical trajectory-based constraints. We illustrate the framework using a biochemical model network of growth factor-based signaling. We also show how to find the closest detailed balanced Markov model to a given Markov model. Further applications and generalizations are discussed.

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

    Science.gov (United States)

    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.

  6. Consistent biases in Antarctic sea ice concentration simulated by climate models

    Science.gov (United States)

    Roach, Lettie A.; Dean, Samuel M.; Renwick, James A.

    2018-01-01

    The simulation of Antarctic sea ice in global climate models often does not agree with observations. In this study, we examine the compactness of sea ice, as well as the regional distribution of sea ice concentration, in climate models from the latest Coupled Model Intercomparison Project (CMIP5) and in satellite observations. We find substantial differences in concentration values between different sets of satellite observations, particularly at high concentrations, requiring careful treatment when comparing to models. As a fraction of total sea ice extent, models simulate too much loose, low-concentration sea ice cover throughout the year, and too little compact, high-concentration cover in the summer. In spite of the differences in physics between models, these tendencies are broadly consistent across the population of 40 CMIP5 simulations, a result not previously highlighted. Separating models with and without an explicit lateral melt term, we find that inclusion of lateral melt may account for overestimation of low-concentration cover. Targeted model experiments with a coupled ocean-sea ice model show that choice of constant floe diameter in the lateral melt scheme can also impact representation of loose ice. This suggests that current sea ice thermodynamics contribute to the inadequate simulation of the low-concentration regime in many models.

  7. Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models: A Discussion and Illustration Using M"plus"

    Science.gov (United States)

    McNeish, Daniel M.

    2016-01-01

    Mixed-effects models (MEMs) and latent growth models (LGMs) are often considered interchangeable save the discipline-specific nomenclature. Software implementations of these models, however, are not interchangeable, particularly with small sample sizes. Restricted maximum likelihood estimation that mitigates small sample bias in MEMs has not been…

  8. Comprehensive ecosystem model-experiment synthesis using multiple datasets at two temperate forest free-air CO2 enrichment experiments: model performance and compensating biases

    Energy Technology Data Exchange (ETDEWEB)

    Walker, Anthony P [ORNL; Hanson, Paul J [ORNL; DeKauwe, Martin G [Macquarie University; Medlyn, Belinda [Macquarie University; Zaehle, S [Max Planck Institute for Biogeochemistry; Asao, Shinichi [Colorado State University, Fort Collins; Dietze, Michael [University of Illinois, Urbana-Champaign; Hickler, Thomas [Goethe University, Frankfurt, Germany; Huntinford, Chris [Centre for Ecology and Hydrology, Wallingford, United Kingdom; Iversen, Colleen M [ORNL; Jain, Atul [University of Illinois, Urbana-Champaign; Lomas, Mark [University of Sheffield; Luo, Yiqi [University of Oklahoma; McCarthy, Heather R [Duke University; Parton, William [Colorado State University, Fort Collins; Prentice, I. Collin [Macquarie University; Thornton, Peter E [ORNL; Wang, Shusen [Canada Centre for Remote Sensing (CCRS); Wang, Yingping [CSIRO Marine and Atmospheric Research; Warlind, David [Lund University, Sweden; Weng, Ensheng [University of Oklahoma, Norman; Warren, Jeffrey [ORNL; Woodward, F. Ian [University of Sheffield; Oren, Ram [Duke University; Norby, Richard J [ORNL

    2014-01-01

    Free Air CO2 Enrichment (FACE) experiments provide a remarkable wealth of data to test the sensitivities of terrestrial ecosystem models (TEMs). In this study, a broad set of 11 TEMs were compared to 22 years of data from two contrasting FACE experiments in temperate forests of the south eastern US the evergreen Duke Forest and the deciduous Oak Ridge forest. We evaluated the models' ability to reproduce observed net primary productivity (NPP), transpiration and Leaf Area index (LAI) in ambient CO2 treatments. Encouragingly, many models simulated annual NPP and transpiration within observed uncertainty. Daily transpiration model errors were often related to errors in leaf area phenology and peak LAI. Our analysis demonstrates that the simulation of LAI often drives the simulation of transpiration and hence there is a need to adopt the most appropriate of hypothesis driven methods to simulate and predict LAI. Of the three competing hypotheses determining peak LAI (1) optimisation to maximise carbon export, (2) increasing SLA with canopy depth and (3) the pipe model the pipe model produced LAI closest to the observations. Modelled phenology was either prescribed or based on broader empirical calibrations to climate. In some cases, simulation accuracy was achieved through compensating biases in component variables. For example, NPP accuracy was sometimes achieved with counter-balancing biases in nitrogen use efficiency and nitrogen uptake. Combined analysis of parallel measurements aides the identification of offsetting biases; without which over-confidence in model abilities to predict ecosystem function may emerge, potentially leading to erroneous predictions of change under future climates.

  9. Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport model

    Science.gov (United States)

    Kuhlmann, G.; Lam, Y. F.; Cheung, H. M.; Hartl, A.; Fung, J. C. H.; Chan, P. W.; Wenig, M. O.

    2015-05-01

    In this paper, we present the custom Hong Kong NO2 retrieval (HKOMI) for the Ozone Monitoring Instrument (OMI) on board the Aura satellite which was used to evaluate a high-resolution chemistry transport model (CTM) (3 km x 3 km spatial resolution). The atmospheric chemistry transport was modelled in the Pearl River Delta (PRD) region in southern China by the Models-3 Community Multiscale Air Quality (CMAQ) modelling system from October 2006 to January 2007. In the HKOMI NO2 retrieval, tropospheric air mass factors (AMFs) were recalculated using high-resolution ancillary parameters of surface reflectance, a priori NO2 and aerosol profiles, of which the latter two were taken from the CMAQ simulation. We tested the influence of the ancillary parameters on the data product using four different aerosol parametrizations. Ground-level measurements by the PRD Regional Air Quality Monitoring (RAQM) network were used as additional independent measurements. The HKOMI retrieval increases estimated tropospheric NO2 vertical column densities (VCD) by (+31 ± 38)%, when compared to NASA's standard product (OMNO2-SP), and improves the normalized mean bias (NMB) between satellite and ground observations by 26 percentage points from -41 to -15%. The individual influences of the parameters are (+11.4 ± 13.4)% for NO2 profiles, (+11.0 ± 20.9)% for surface reflectance and (+6.0 ± 8.4)% for the best aerosol parametrization. The correlation coefficient r is low between ground and satellite observations (r = 0.35). The low r and the remaining NMB can be explained by the low model performance and the expected differences when comparing point measurements with area-averaged satellite observations. The correlation between CMAQ and the RAQM network is low (r ~ 0.3) and the model underestimates the NO2 concentrations in the northwestern model domain (Foshan and Guangzhou). We compared the CMAQ NO2 time series of the two main plumes with our best OMI NO2 data set (HKOMI-4). The model

  10. Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota.

    Energy Technology Data Exchange (ETDEWEB)

    Portone, Teresa; Niederhaus, John Henry; Sanchez, Jason James; Swiler, Laura Painton

    2018-02-01

    This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.

  11. 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

  12. Referee Bias

    OpenAIRE

    Dohmen, Thomas; Sauermann, Jan

    2015-01-01

    This paper surveys the empirical literature on the behavior of referees in professional football and other sports. Referees are typically appointed by a principal to be impartial, especially when unbiased referee judgment is vital for the accomplishment of the principal's objective. Answering whether referees make biased decisions and understanding the causes that lead referees to digress from their principal duty of impartiality is therefore fundamental from a theoretical point of view. At t...

  13. Bayesian Estimation of Circumplex Models Subject to Prior Theory Constraints and Scale-Usage Bias

    Science.gov (United States)

    Lenk, Peter; Wedel, Michel; Bockenholt, Ulf

    2006-01-01

    This paper presents a hierarchical Bayes circumplex model for ordinal ratings data. The circumplex model was proposed to represent the circular ordering of items in psychological testing by imposing inequalities on the correlations of the items. We provide a specification of the circumplex, propose identifying constraints and conjugate priors for…

  14. A low-bias simulation scheme for the SABR stochastic volatility model

    NARCIS (Netherlands)

    B. Chen (Bin); C.W. Oosterlee (Cornelis); J.A.M. van der Weide

    2012-01-01

    htmlabstractThe Stochastic Alpha Beta Rho Stochastic Volatility (SABR-SV) model is widely used in the financial industry for the pricing of fixed income instruments. In this paper we develop an lowbias simulation scheme for the SABR-SV model, which deals efficiently with (undesired)

  15. A Hybrid Multiple Criteria Decision Making Model for Supplier Selection

    OpenAIRE

    Wu, Chung-Min; Hsieh, Ching-Lin; Chang, Kuei-Lun

    2013-01-01

    The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM) model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain their weights. To avoid calculation and additional pairwise compa...

  16. Astrophysical Model Selection in Gravitational Wave Astronomy

    Science.gov (United States)

    Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.

    2012-01-01

    Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.

  17. Drop-out from cardiovascular magnetic resonance in a randomized controlled trial of ST-elevation myocardial infarction does not cause selection bias on endpoints

    DEFF Research Database (Denmark)

    Laursen, Peter Nørkjær; Holmvang, L.; Kelbæk, H.

    2017-01-01

    Background: The extent of selection bias due to drop-out in clinical trials of ST-elevation myocardial infarction (STEMI) using cardiovascular magnetic resonance (CMR) as surrogate endpoints is unknown. We sought to interrogate the characteristics and prognosis of patients who dropped out before...... as evaluated by the TIMI-risk score (3.7 (± 2.1) vs 4.0 (± 2.6), p = 0.043) and by left ventricular ejection fraction (43 (± 9) vs. 47 (± 10), p = 0.029). CMR drop-outs had a higher incidence of known hypertension (39% vs. 35%, p = 0.043), known diabetes (14% vs. 7%, p = 0.025), known cardiac disease (11% vs....... 3%, p = 0.013) and known renal function disease (5% vs. 0%, p = 0.007). However, the 30-day and 5-years composite endpoint rate was not significantly higher among the CMR drop-out ((HR 1.43 (95%-CI 0.5; 3.97) (p = 0.5)) and (HR 1.31 (95%-CI 0.84; 2.05) (p = 0.24)). Conclusion: CMR-drop-outs had...

  18. Kaon and pion production in centrality selected minimum bias Pb+Pb collisions at 40 and 158A.GeV

    CERN Document Server

    Dinkelaker, Peter

    2009-01-01

    Results on charged kaon and negatively charged pion production and spectra for centrality selected Pb+Pb mininimum bias events at 40 and 158A GeV have been presented in this thesis. All analysis are based on data taken by the NA49 experiment at the accelerator Super Proton Synchrotron (SPS) at the European Organization for Nuclear Research (CERN) in Geneva, Switzerland. The kaon results are based on an analysis of the mean energy loss of the charged particles traversing the detector gas of the time projection chambers (TPCs). The pion results are from an analysis of all negatively charged particles h- corrected for contributions from particle decays and secondary interactions. For the dE/dx analysis of charged kaons, main TPC tracks with a total momentum between 4 and 50 GeV have been analyzed in logarithmic momentum log(p) and transverse momentum pt bins. The resulting dE/dx spectra have been fitted by the sum of 5 Gaussians, one for each main particle type (electrons, pions, kaons, pro...

  19. Modeling and Analysis of Supplier Selection Method Using ...

    African Journals Online (AJOL)

    However, in these parts of the world the application of tools and models for supplier selection problem is yet to surface and the banking and finance industry here in Ethiopia is no exception. Thus, the purpose of this research was to address supplier selection problem through modeling and application of analytical hierarchy ...

  20. Selection bias from sampling frames: telephone directory and electoral roll compared with door-to-door population census: results from the Blue Mountains Eye Study.

    Science.gov (United States)

    Smith, W; Mitchell, P; Attebo, K; Leeder, S

    1997-04-01

    Many Australian public health research studies use the telephone directory or the electoral roll as a sampling frame from which to draw study subjects. The sociodemographic, disease-state and risk-factor characteristics of subjects who could be recruited using only the telephone directory or only the electoral roll sampling frames were compared with the characteristics of subjects who would have been missed using only these sampling frames, respectively. In the first phase of the Blue Mountains Eye Study we interviewed and examined 2557 people aged 49 and over living in a defined postcode area, recruited from a door-to-door census. This represented a participation rate of 80.9 per cent and a response rate of 87.9 per cent. The telephone directory was searched for each subject's telephone number and the electoral roll was searched for each subject. Subject characteristics for those who were present in each of these sampling frames were compared with the characteristics of those subjects not included in the sampling frames. The telephone directory listed 2102 (82.2 per cent) of the subjects, and 115 (4.5 per cent) had no telephone connected. The electoral roll contained 2156 (84.3 per cent) of the subjects, and 141 subjects (5.5 per cent) could not be found in either the electoral roll or the telephone directory. Younger subjects, subjects who did not own their own homes and subjects born outside of Australia were significantly less likely to be included in either of these sampling frames. The telephone directory was also more likely to exclude subjects with higher occupational prestige, while the electoral roll was more likely to exclude unmarried persons and males. Researchers using the telephone directory and electoral roll to select subjects for study should be aware of the potential selection bias these sampling frames incur and need to take care when generalising their findings to the wider community.

  1. On Optimal Input Design and Model Selection for Communication Channels

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  2. Python Program to Select HII Region Models

    Science.gov (United States)

    Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.

    2016-01-01

    HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.

  3. Ground-water transport model selection and evaluation guidelines

    International Nuclear Information System (INIS)

    Simmons, C.S.; Cole, C.R.

    1983-01-01

    Guidelines are being developed to assist potential users with selecting appropriate computer codes for ground-water contaminant transport modeling. The guidelines are meant to assist managers with selecting appropriate predictive models for evaluating either arid or humid low-level radioactive waste burial sites. Evaluation test cases in the form of analytical solutions to fundamental equations and experimental data sets have been identified and recommended to ensure adequate code selection, based on accurate simulation of relevant physical processes. The recommended evaluation procedures will consider certain technical issues related to the present limitations in transport modeling capabilities. A code-selection plan will depend on identifying problem objectives, determining the extent of collectible site-specific data, and developing a site-specific conceptual model for the involved hydrology. Code selection will be predicated on steps for developing an appropriate systems model. This paper will review the progress in developing those guidelines. 12 references

  4. Model and Variable Selection Procedures for Semiparametric Time Series Regression

    Directory of Open Access Journals (Sweden)

    Risa Kato

    2009-01-01

    Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.

  5. Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming

    Science.gov (United States)

    Rosenblum, Erica; Eisenman, Ian

    2017-08-01

    Observations indicate that the Arctic sea ice cover is rapidly retreating while the Antarctic sea ice cover is steadily expanding. State-of-the-art climate models, by contrast, typically simulate a moderate decrease in both the Arctic and Antarctic sea ice covers. However, in each hemisphere there is a small subset of model simulations that have sea ice trends similar to the observations. Based on this, a number of recent studies have suggested that the models are consistent with the observations in each hemisphere when simulated internal climate variability is taken into account. Here we examine sea ice changes during 1979-2013 in simulations from the most recent Coupled Model Intercomparison Project (CMIP5) as well as the Community Earth System Model Large Ensemble (CESM-LE), drawing on previous work that found a close relationship in climate models between global-mean surface temperature and sea ice extent. We find that all of the simulations with 1979-2013 Arctic sea ice retreat as fast as observed have considerably more global warming than observations during this time period. Using two separate methods to estimate the sea ice retreat that would occur under the observed level of global warming in each simulation in both ensembles, we find that simulated Arctic sea ice retreat as fast as observed would occur less than 1% of the time. This implies that the models are not consistent with the observations. In the Antarctic, we find that simulated sea ice expansion as fast as observed typically corresponds with too little global warming, although these results are more equivocal. We show that because of this, the simulations do not capture the observed asymmetry between Arctic and Antarctic sea ice trends. This suggests that the models may be getting the right sea ice trends for the wrong reasons in both polar regions.

  6. Do forest gap model simulations of climate change effects have a catastrophist bias?

    Energy Technology Data Exchange (ETDEWEB)

    LeBlanc, D.C. [Ball State Univ., Muncie, IN (United States). Dept. of Biology; Loehle, C. [Argonne National Lab., IL (United States)

    1997-08-01

    Forest gap simulation models have been widely used to project potential effects of global climate change on forest ecosystems. These projections are given high visibility in governmental and intergovernmental reports that are part of the basis for formulating national and international policy regarding global change. The most recent draft report from the IPCC is but one of many cases where the results from forest gap simulation models have been used as the best available projection of the effects of climate change on forests.

  7. Methods for model selection in applied science and engineering.

    Energy Technology Data Exchange (ETDEWEB)

    Field, Richard V., Jr.

    2004-10-01

    Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be

  8. Are Fit Indices Biased in Favor of Bi-Factor Models in Cognitive Ability Research?: A Comparison of Fit in Correlated Factors, Higher-Order, and Bi-Factor Models via Monte Carlo Simulations

    Directory of Open Access Journals (Sweden)

    Grant B. Morgan

    2015-02-01

    Full Text Available Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.

  9. General three-state model with biased population replacement: analytical solution and application to language dynamics.

    Science.gov (United States)

    Colaiori, Francesca; Castellano, Claudio; Cuskley, Christine F; Loreto, Vittorio; Pugliese, Martina; Tria, Francesca

    2015-01-01

    Empirical evidence shows that the rate of irregular usage of English verbs exhibits discontinuity as a function of their frequency: the most frequent verbs tend to be totally irregular. We aim to qualitatively understand the origin of this feature by studying simple agent-based models of language dynamics, where each agent adopts an inflectional state for a verb and may change it upon interaction with other agents. At the same time, agents are replaced at some rate by new agents adopting the regular form. In models with only two inflectional states (regular and irregular), we observe that either all verbs regularize irrespective of their frequency, or a continuous transition occurs between a low-frequency state, where the lemma becomes fully regular, and a high-frequency one, where both forms coexist. Introducing a third (mixed) state, wherein agents may use either form, we find that a third, qualitatively different behavior may emerge, namely, a discontinuous transition in frequency. We introduce and solve analytically a very general class of three-state models that allows us to fully understand these behaviors in a unified framework. Realistic sets of interaction rules, including the well-known naming game (NG) model, result in a discontinuous transition, in agreement with recent empirical findings. We also point out that the distinction between speaker and hearer in the interaction has no effect on the collective behavior. The results for the general three-state model, although discussed in terms of language dynamics, are widely applicable.

  10. Modeling Exon-Specific Bias Distribution Improves the Analysis of RNA-Seq Data.

    Directory of Open Access Journals (Sweden)

    Xuejun Liu

    Full Text Available RNA-seq technology has become an important tool for quantifying the gene and transcript expression in transcriptome study. The two major difficulties for the gene and transcript expression quantification are the read mapping ambiguity and the overdispersion of the read distribution along reference sequence. Many approaches have been proposed to deal with these difficulties. A number of existing methods use Poisson distribution to model the read counts and this easily splits the counts into the contributions from multiple transcripts. Meanwhile, various solutions were put forward to account for the overdispersion in the Poisson models. By checking the similarities among the variation patterns of read counts for individual genes, we found that the count variation is exon-specific and has the conserved pattern across the samples for each individual gene. We introduce Gamma-distributed latent variables to model the read sequencing preference for each exon. These variables are embedded to the rate parameter of a Poisson model to account for the overdispersion of read distribution. The model is tractable since the Gamma priors can be integrated out in the maximum likelihood estimation. We evaluate the proposed approach, PGseq, using four real datasets and one simulated dataset, and compare its performance with other popular methods. Results show that PGseq presents competitive performance compared to other alternatives in terms of accuracy in the gene and transcript expression calculation and in the downstream differential expression analysis. Especially, we show the advantage of our method in the analysis of low expression.

  11. Important limitations in the modeling of activated sludge : biased calibration of the hydrolysis process

    DEFF Research Database (Denmark)

    Insel, G.; Gul, Ö.K.; Orhon, D.

    2002-01-01

    ), provides the majority of the required experimental database. However, currently used procedures still involve a number of basic and practical problems. Model evaluation of the OUR data may generate a distorted image of the processes involved. Hydrolysis is the most important, yet the most vulnerable...... process as far as the experimental assessment of accurate kinetic parameters is concerned. This study intends to provide an overview of major experimental limitations in the modeling of activated sludge, with emphasis on the appropriate experimental design for the assessment of the hydrolysis rate....

  12. Random effect selection in generalised linear models

    DEFF Research Database (Denmark)

    Denwood, Matt; Houe, Hans; Forkman, Björn

    We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...

  13. Modeling a Negative Response Bias in the Human Amygdala by Noradrenergic-Glucocorticoid Interactions

    NARCIS (Netherlands)

    Kukolja, Juraj; Schlaepfer, Thomas E.; Keysers, Christian; Klingmueller, Dietrich; Maier, Wolfgang; Fink, Gereon R.; Hurlemann, Rene

    2008-01-01

    An emerging theme in the neuroscience of emotion is the question of how acute stress shapes, and distorts, social-emotional behavior. The prevailing neurocircuitry models of social-emotional behavior emphasize the central role of the amygdala. Acute stress leads to increased central levels of

  14. Important limitations in the modeling of activated sludge : biased calibration of the hydrolysis process

    DEFF Research Database (Denmark)

    Insel, G.; Gul, Ö.K.; Orhon, D.

    2002-01-01

    ), provides the majority of the required experimental database. However, currently used procedures still involve a number of basic and practical problems. Model evaluation of the OUR data may generate a distorted image of the processes involved. Hydrolysis is the most important, yet the most vulnerable...

  15. Bias and uncertainty of δ13CO2 isotopic mixing models

    Science.gov (United States)

    Zachary E. Kayler; Lisa Ganio; Mark Hauck; Thomas G. Pypker; Elizabeth W. Sulzman; Alan C. Mix; Barbara J. Bond

    2009-01-01

    The goal of this study was to evaluate how factorial combinations of two mixing models and two regression approaches (Keeling-OLS, Miller—Tans-OLS, Keeling-GMR, Miller—Tans-GMR) compare in small [CO2] range versus large[CO2] range regimes, with different combinations of...

  16. Forces that influence the evolution of codon bias

    Science.gov (United States)

    Sharp, Paul M.; Emery, Laura R.; Zeng, Kai

    2010-01-01

    The frequencies of alternative synonymous codons vary both among species and among genes from the same genome. These patterns have been inferred to reflect the action of natural selection. Here we evaluate this in bacteria. While intragenomic variation in many species is consistent with selection favouring translationally optimal codons, much of the variation among species appears to be due to biased patterns of mutation. The strength of selection on codon usage can be estimated by two different approaches. First, the extent of bias in favour of translationally optimal codons in highly expressed genes, compared to that in genes where selection is weak, reveals the long-term effectiveness of selection. Here we show that the strength of selected codon usage bias is highly correlated with bacterial growth rate, suggesting that selection has favoured translational efficiency. Second, the pattern of bias towards optimal codons at polymorphic sites reveals the ongoing action of selection. Using this approach we obtained results that were completely consistent with the first method; importantly, the frequency spectra of optimal codons at polymorphic sites were similar to those predicted under an equilibrium model. Highly expressed genes in Escherichia coli appear to be under continuing strong selection, whereas selection is very weak in genes expressed at low levels. PMID:20308095

  17. The genealogy of samples in models with selection.

    Science.gov (United States)

    Neuhauser, C; Krone, S M

    1997-02-01

    We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.

  18. Exploring and accounting for publication bias in mental health: a brief overview of methods.

    Science.gov (United States)

    Mavridis, Dimitris; Salanti, Georgia

    2014-02-01

    OBJECTIVE Publication bias undermines the integrity of published research. The aim of this paper is to present a synopsis of methods for exploring and accounting for publication bias. METHODS We discussed the main features of the following methods to assess publication bias: funnel plot analysis; trim-and-fill methods; regression techniques and selection models. We applied these methods to a well-known example of antidepressants trials that compared trials submitted to the Food and Drug Administration (FDA) for regulatory approval. RESULTS The funnel plot-related methods (visual inspection, trim-and-fill, regression models) revealed an association between effect size and SE. Contours of statistical significance showed that asymmetry in the funnel plot is probably due to publication bias. Selection model found a significant correlation between effect size and propensity for publication. CONCLUSIONS Researchers should always consider the possible impact of publication bias. Funnel plot-related methods should be seen as a means of examining for small-study effects and not be directly equated with publication bias. Possible causes for funnel plot asymmetry should be explored. Contours of statistical significance may help disentangle whether asymmetry in a funnel plot is caused by publication bias or not. Selection models, although underused, could be useful resource when publication bias and heterogeneity are suspected because they address directly the problem of publication bias and not that of small-study effects.

  19. Model of 1/f noise in ion implanted resistors as a function of the resistance, determined by a reverse bias voltage

    International Nuclear Information System (INIS)

    Beck, H.G.E.

    1979-01-01

    A model is presented for the 1/f noise in ion-implanted resistors. The resistance was changed by a reverse bias voltage. The model explains the experimentally found square dependence between the relative 1/f noise intensity C/C 0 and the relative change in resistance R/R 0 . (author)

  20. 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......" conformation is too simple. The current explanation for this biased agonism is that GPCRs can adopt multiple active conformations stabilized by different molecules, and that each conformation affects intracellular signaling in a different way. In the present study we sought to investigate biased agonism...

  1. Modeling shape selection of buckled dielectric elastomers

    Science.gov (United States)

    Langham, Jacob; Bense, Hadrien; Barkley, Dwight

    2018-02-01

    A dielectric elastomer whose edges are held fixed will buckle, given a sufficiently applied voltage, resulting in a nontrivial out-of-plane deformation. We study this situation numerically using a nonlinear elastic model which decouples two of the principal electrostatic stresses acting on an elastomer: normal pressure due to the mutual attraction of oppositely charged electrodes and tangential shear ("fringing") due to repulsion of like charges at the electrode edges. These enter via physically simplified boundary conditions that are applied in a fixed reference domain using a nondimensional approach. The method is valid for small to moderate strains and is straightforward to implement in a generic nonlinear elasticity code. We validate the model by directly comparing the simulated equilibrium shapes with the experiment. For circular electrodes which buckle axisymetrically, the shape of the deflection profile is captured. Annular electrodes of different widths produce azimuthal ripples with wavelengths that match our simulations. In this case, it is essential to compute multiple equilibria because the first model solution obtained by the nonlinear solver (Newton's method) is often not the energetically favored state. We address this using a numerical technique known as "deflation." Finally, we observe the large number of different solutions that may be obtained for the case of a long rectangular strip.

  2. Modeling HIV-1 drug resistance as episodic directional selection.

    Directory of Open Access Journals (Sweden)

    Ben Murrell

    Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

  3. Variable selection for mixture and promotion time cure rate models.

    Science.gov (United States)

    Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng

    2016-11-16

    Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.

  4. Citation bias and selective focus on positive findings in the literature on the serotonin transporter gene (5-HTTLPR), life stress and depression.

    Science.gov (United States)

    de Vries, Y A; Roest, A M; Franzen, M; Munafò, M R; Bastiaansen, J A

    2016-10-01

    Caspi et al.'s 2003 report that 5-HTTLPR genotype moderates the influence of life stress on depression has been highly influential but remains contentious. We examined whether the evidence base for the 5-HTTLPR-stress interaction has been distorted by citation bias and a selective focus on positive findings. A total of 73 primary studies were coded for study outcomes and focus on positive findings in the abstract. Citation rates were compared between studies with positive and negative results, both within this network of primary studies and in Web of Science. In addition, the impact of focus on citation rates was examined. In all, 24 (33%) studies were coded as positive, but these received 48% of within-network and 68% of Web of Science citations. The 38 (52%) negative studies received 42 and 23% of citations, respectively, while the 11 (15%) unclear studies received 10 and 9%. Of the negative studies, the 16 studies without a positive focus (42%) received 47% of within-network citations and 32% of Web of Science citations, while the 13 (34%) studies with a positive focus received 39 and 51%, respectively, and the nine (24%) studies with a partially positive focus received 14 and 17%. Negative studies received fewer citations than positive studies. Furthermore, over half of the negative studies had a (partially) positive focus, and Web of Science citation rates were higher for these studies. Thus, discussion of the 5-HTTLPR-stress interaction is more positive than warranted. This study exemplifies how evidence-base-distorting mechanisms undermine the authenticity of research findings.

  5. Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain

    OpenAIRE

    Feipeng Guo; Qibei Lu

    2013-01-01

    With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...

  6. Potential self-selection bias in a nested case-control study on indoor environmental factors and their association with asthma and allergic symptoms among pre-school children

    DEFF Research Database (Denmark)

    Bornehag, Carl-Gustaf; Sundell, Jan; Sigsgaard, T.

    2006-01-01

    , including health, building characteristics of the home, and socioeconomic factors between participating and non-participating families in a nested case-control study on asthma and allergy among children. Information was collected in a baseline questionnaire to the parents of 14,077 children aged 1-6 years...... in a first step. In a second step 2,156 of the children were invited to participate in a case-control study. Of these, 198 cases and 202 controls were finally selected. For identifying potential selection bias, information concerning all invited families in the case-control study was obtained from...... the baseline questionnaire. Results show that there are several possible biases due to self-selection involved in an extensive study on the impact of the home environment on asthma and allergy among children. Factors associated with participating were high socioeconomic status of the family, more health...

  7. Connecting to Get Things Done: A Conceptual Model of the Process Used to Respond to Bias Incidents

    Science.gov (United States)

    LePeau, Lucy A.; Morgan, Demetri L.; Zimmerman, Hilary B.; Snipes, Jeremy T.; Marcotte, Beth A.

    2016-01-01

    In this study, we interviewed victims of bias incidents and members of a bias response team to investigate the process the team used to respond to incidents. Incidents included acts of sexism, homophobia, and racism on a large, predominantly White research university in the Midwest. Data were analyzed using a 4-stage coding process. The emergent…

  8. Is There Room for "Development" in Developmental Models of Information Processing Biases to Threat in Children and Adolescents?

    Science.gov (United States)

    Field, Andy P.; Lester, Kathryn J.

    2010-01-01

    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…

  9. Can Service Learning Reinforce Social and Cultural Bias? Exploring a Popular Model of Family Involvement for Early Childhood Teacher Candidates

    Science.gov (United States)

    Dunn-Kenney, Maylan

    2010-01-01

    Service learning is often used in teacher education as a way to challenge social bias and provide teacher candidates with skills needed to work in partnership with diverse families. Although some literature suggests that service learning could reinforce cultural bias, there is little documentation. In a study of 21 early childhood teacher…

  10. Systematic errors in detecting biased agonism: Analysis of current methods and development of a new model-free approach.

    Science.gov (United States)

    Onaran, H Ongun; Ambrosio, Caterina; Uğur, Özlem; Madaras Koncz, Erzsebet; Grò, Maria Cristina; Vezzi, Vanessa; Rajagopal, Sudarshan; Costa, Tommaso

    2017-03-14

    Discovering biased agonists requires a method that can reliably distinguish the bias in signalling due to unbalanced activation of diverse transduction proteins from that of differential amplification inherent to the system being studied, which invariably results from the non-linear nature of biological signalling networks and their measurement. We have systematically compared the performance of seven methods of bias diagnostics, all of which are based on the analysis of concentration-response curves of ligands according to classical receptor theory. We computed bias factors for a number of β-adrenergic agonists by comparing BRET assays of receptor-transducer interactions with Gs, Gi and arrestin. Using the same ligands, we also compared responses at signalling steps originated from the same receptor-transducer interaction, among which no biased efficacy is theoretically possible. In either case, we found a high level of false positive results and a general lack of correlation among methods. Altogether this analysis shows that all tested methods, including some of the most widely used in the literature, fail to distinguish true ligand bias from "system bias" with confidence. We also propose two novel semi quantitative methods of bias diagnostics that appear to be more robust and reliable than currently available strategies.

  11. Effect of Model Selection on Computed Water Balance Components

    NARCIS (Netherlands)

    Jhorar, R.K.; Smit, A.A.M.F.R.; Roest, C.W.J.

    2009-01-01

    Soil water flow modelling approaches as used in four selected on-farm water management models, namely CROPWAT. FAIDS, CERES and SWAP, are compared through numerical experiments. The soil water simulation approaches used in the first three models are reformulated to incorporate ail evapotranspiration

  12. Ensembling Variable Selectors by Stability Selection for the Cox Model

    Directory of Open Access Journals (Sweden)

    Qing-Yan Yin

    2017-01-01

    Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

  13. Elementary Teachers' Selection and Use of Visual Models

    Science.gov (United States)

    Lee, Tammy D.; Gail Jones, M.

    2018-02-01

    As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.

  14. Validation of elk resource selection models with spatially independent data

    Science.gov (United States)

    Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

    2011-01-01

    Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

  15. A Working Model of Natural Selection Illustrated by Table Tennis

    Science.gov (United States)

    Dinc, Muhittin; Kilic, Selda; Aladag, Caner

    2013-01-01

    Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…

  16. Augmented Self-Modeling as an Intervention for Selective Mutism

    Science.gov (United States)

    Kehle, Thomas J.; Bray, Melissa A.; Byer-Alcorace, Gabriel F.; Theodore, Lea A.; Kovac, Lisa M.

    2012-01-01

    Selective mutism is a rare disorder that is difficult to treat. It is often associated with oppositional defiant behavior, particularly in the home setting, social phobia, and, at times, autism spectrum disorder characteristics. The augmented self-modeling treatment has been relatively successful in promoting rapid diminishment of selective mutism…

  17. Reducing Bias of Allele Frequency Estimates by Modeling SNP Genotype Data with Informative Missingness

    Directory of Open Access Journals (Sweden)

    Wan-Yu eLin

    2012-06-01

    Full Text Available The presence of missing single-nucleotide polymorphism (SNP genotypes is common in genetic data. For studies with low-density SNPs, the most commonly used approach to deal with genotype missingness is to simply remove the observations with missing genotypes from the analyses. This naïve method is straightforward but is appropriate only when the missingness is random. However, a given assay often has a different capability in genotyping heterozygotes and homozygotes, causing the phenomenon of ‘differential dropout’ in the sense that the missing rates of heterozygotes and homozygotes are different. In practice, differential dropout among genotypes exists in even carefully designed studies, such as the data from the HapMap project and the Wellcome Trust Case Control Consortium. In this study, we propose a statistical method to model the differential dropout among different genotypes. Compared with the naïve method, our method provides more accurate allele frequency estimates when the differential dropout is present. To demonstrate its practical use, we further apply our method to the HapMap data and a scleroderma data set.

  18. Robust Decision-making Applied to Model Selection

    Energy Technology Data Exchange (ETDEWEB)

    Hemez, Francois M. [Los Alamos National Laboratory

    2012-08-06

    The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical