Sample size estimation and sampling techniques for selecting a representative sample

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Aamir Omair

2014-01-01

Full Text Available Introduction: The purpose of this article is to provide a general understanding of the concepts of sampling as applied to health-related research. Sample Size Estimation: It is important to select a representative sample in quantitative research in order to be able to generalize the results to the target population. The sample should be of the required sample size and must be selected using an appropriate probability sampling technique. There are many hidden biases which can adversely affect the outcome of the study. Important factors to consider for estimating the sample size include the size of the study population, confidence level, expected proportion of the outcome variable (for categorical variables/standard deviation of the outcome variable (for numerical variables, and the required precision (margin of accuracy from the study. The more the precision required, the greater is the required sample size. Sampling Techniques: The probability sampling techniques applied for health related research include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. These are more recommended than the nonprobability sampling techniques, because the results of the study can be generalized to the target population.

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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

Science.gov (United States)

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

2009-12-01

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

Sample Selection for Training Cascade Detectors.

Science.gov (United States)

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

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.

Sample Selection for Training Cascade Detectors.

Directory of Open Access Journals (Sweden)

Noelia Vállez

Full Text Available Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

Rational Learning and Information Sampling: On the "Naivety" Assumption in Sampling Explanations of Judgment Biases

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Le Mens, Gael; Denrell, Jerker

2011-01-01

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them.…

Sampling bias in climate-conflict research

Science.gov (United States)

Adams, Courtland; Ide, Tobias; Barnett, Jon; Detges, Adrien

2018-03-01

Critics have argued that the evidence of an association between climate change and conflict is flawed because the research relies on a dependent variable sampling strategy1-4. Similarly, it has been hypothesized that convenience of access biases the sample of cases studied (the `streetlight effect'5). This also gives rise to claims that the climate-conflict literature stigmatizes some places as being more `naturally' violent6-8. Yet there has been no proof of such sampling patterns. Here we test whether climate-conflict research is based on such a biased sample through a systematic review of the literature. We demonstrate that research on climate change and violent conflict suffers from a streetlight effect. Further, studies which focus on a small number of cases in particular are strongly informed by cases where there has been conflict, do not sample on the independent variables (climate impact or risk), and hence tend to find some association between these two variables. These biases mean that research on climate change and conflict primarily focuses on a few accessible regions, overstates the links between both phenomena and cannot explain peaceful outcomes from climate change. This could result in maladaptive responses in those places that are stigmatized as being inherently more prone to climate-induced violence.

Bias Assessment of General Chemistry Analytes using Commutable Samples.

Science.gov (United States)

Koerbin, Gus; Tate, Jillian R; Ryan, Julie; Jones, Graham Rd; Sikaris, Ken A; Kanowski, David; Reed, Maxine; Gill, Janice; Koumantakis, George; Yen, Tina; St John, Andrew; Hickman, Peter E; Simpson, Aaron; Graham, Peter

2014-11-01

Harmonisation of reference intervals for routine general chemistry analytes has been a goal for many years. Analytical bias may prevent this harmonisation. To determine if analytical bias is present when comparing methods, the use of commutable samples, or samples that have the same properties as the clinical samples routinely analysed, should be used as reference samples to eliminate the possibility of matrix effect. The use of commutable samples has improved the identification of unacceptable analytical performance in the Netherlands and Spain. The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) has undertaken a pilot study using commutable samples in an attempt to determine not only country specific reference intervals but to make them comparable between countries. Australia and New Zealand, through the Australasian Association of Clinical Biochemists (AACB), have also undertaken an assessment of analytical bias using commutable samples and determined that of the 27 general chemistry analytes studied, 19 showed sufficiently small between method biases as to not prevent harmonisation of reference intervals. Application of evidence based approaches including the determination of analytical bias using commutable material is necessary when seeking to harmonise reference intervals.

Sampling of temporal networks: Methods and biases

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

2017-11-01

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

Rational learning and information sampling: on the "naivety" assumption in sampling explanations of judgment biases.

Science.gov (United States)

Le Mens, Gaël; Denrell, Jerker

2011-04-01

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them. Here, we show that this "naivety" assumption is not necessary. Systematically biased judgments can emerge even when decision makers process available information perfectly and are also aware of how the information sample has been generated. Specifically, we develop a rational analysis of Denrell's (2005) experience sampling model, and we prove that when information search is interested rather than disinterested, even rational information sampling and processing can give rise to systematic patterns of errors in judgments. Our results illustrate that a tendency to favor alternatives for which outcome information is more accessible can be consistent with rational behavior. The model offers a rational explanation for behaviors that had previously been attributed to cognitive and motivational biases, such as the in-group bias or the tendency to prefer popular alternatives. 2011 APA, all rights reserved

The quasar luminosity function from a variability-selected sample

Science.gov (United States)

Hawkins, M. R. S.; Veron, P.

1993-01-01

A sample of quasars is selected from a 10-yr sequence of 30 UK Schmidt plates. Luminosity functions are derived in several redshift intervals, which in each case show a featureless power-law rise towards low luminosities. There is no sign of the 'break' found in the recent UVX sample of Boyle et al. It is suggested that reasons for the disagreement are connected with biases in the selection of the UVX sample. The question of the nature of quasar evolution appears to be still unresolved.

Sampling bias in an internet treatment trial for depression.

Science.gov (United States)

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

2012-10-23

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

Biased sampling, over-identified parameter problems and beyond

CERN Document Server

Qin, Jing

2017-01-01

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

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.

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

Science.gov (United States)

Werthmann, Jessica; Jansen, Anita; Roefs, Anne

2015-05-01

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

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

OpenAIRE

Giovanna Nicolini; Luciana Dalla Valle

2009-01-01

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

Robust inference in sample selection models

KAUST Repository

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

2015-01-01

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

Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail

2015-11-20

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

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

Science.gov (United States)

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

1982-01-01

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

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

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.

Monitoring the aftermath of Flint drinking water contamination crisis: Another case of sampling bias?

Science.gov (United States)

Goovaerts, Pierre

2017-07-15

The delay in reporting high levels of lead in Flint drinking water, following the city's switch to the Flint River as its water supply, was partially caused by the biased selection of sampling sites away from the lead pipe network. Since Flint returned to its pre-crisis source of drinking water, the State has been monitoring water lead levels (WLL) at selected "sentinel" sites. In a first phase that lasted two months, 739 residences were sampled, most of them bi-weekly, to determine the general health of the distribution system and to track temporal changes in lead levels. During the same period, water samples were also collected through a voluntary program whereby concerned citizens received free testing kits and conducted sampling on their own. State officials relied on the former data to demonstrate the steady improvement in water quality. A recent analysis of data collected by voluntary sampling revealed, however, an opposite trend with lead levels increasing over time. This paper looks at potential sampling bias to explain such differences. Although houses with higher WLL were more likely to be sampled repeatedly, voluntary sampling turned out to reproduce fairly well the main characteristics (i.e. presence of lead service lines (LSL), construction year) of Flint housing stock. State-controlled sampling was less representative; e.g., sentinel sites with LSL were mostly built between 1935 and 1950 in lower poverty areas, which might hamper our ability to disentangle the effects of LSL and premise plumbing (lead fixtures and pipes present within old houses) on WLL. Also, there was no sentinel site with LSL in two of the most impoverished wards, including where the percentage of children with elevated blood lead levels tripled following the switch in water supply. Correcting for sampling bias narrowed the gap between sampling programs, yet overall temporal trends are still opposite. Copyright © 2017 Elsevier B.V. All rights reserved.

Personality Traits and Susceptibility to Behavioral Biases among a Sample of Polish Stock Market Investors

Directory of Open Access Journals (Sweden)

Rzeszutek Marcin

2015-09-01

Full Text Available The aim of this paper is to investigate whether susceptibility to selected behavioral biases (overconfidence, mental accounting and sunk-cost fallacy is correlated with the Eysenck’s [1978] personality traits (impulsivity, venturesomeness, and empathy. This study was conducted on a sample of 90 retail investors frequently investing on the Warsaw Stock Exchange. Participants filled out a survey made up of two parts: 1 three situational exercises, which assessed susceptibility to behavioral biases and 2 an Impulsiveness Questionnaire, which measures impulsivity, venturesomeness, and empathy. The results demonstrated the relationship between venturesomeness and susceptibility to all behavioral biases explored in this study. We find that higher level of venturesomeness was linked with a lower probability of all behavioral biases included in this study.

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.

Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

Science.gov (United States)

Verdery, Ashton M; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J

2015-01-01

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

Biased selection within the social health insurance market in Colombia.

Science.gov (United States)

Castano, Ramon; Zambrano, Andres

2006-12-01

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

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

Science.gov (United States)

Dobie, Robert A

2009-12-01

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

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.

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

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

2015-02-01

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

Adaptive enhanced sampling by force-biasing using neural networks

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Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Characterizing sampling and quality screening biases in infrared and microwave limb sounding

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Millán, Luis F.; Livesey, Nathaniel J.; Santee, Michelle L.; von Clarmann, Thomas

2018-03-01

This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected. The results show that upper-tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper-tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although its penetration is limited to the upper troposphere, while MIPAS may cover well into the mid-troposphere in cloud-free scenarios. We emphasize that the

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…

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.

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

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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).

The effect of DNA degradation bias in passive sampling devices on metabarcoding studies of arthropod communities and their associated microbiota.

Science.gov (United States)

Krehenwinkel, Henrik; Fong, Marisa; Kennedy, Susan; Huang, Edward Greg; Noriyuki, Suzuki; Cayetano, Luis; Gillespie, Rosemary

2018-01-01

PCR amplification bias is a well-known problem in metagenomic analysis of arthropod communities. In contrast, variation of DNA degradation rates is a largely neglected source of bias. Differential degradation of DNA molecules could cause underrepresentation of taxa in a community sequencing sample. Arthropods are often collected by passive sampling devices, like malaise traps. Specimens in such a trap are exposed to varying periods of suboptimal storage and possibly different rates of DNA degradation. Degradation bias could thus be a significant issue, skewing diversity estimates. Here, we estimate the effect of differential DNA degradation on the recovery of community diversity of Hawaiian arthropods and their associated microbiota. We use a simple DNA size selection protocol to test for degradation bias in mock communities, as well as passively collected samples from actual Malaise traps. We compare the effect of DNA degradation to that of varying PCR conditions, including primer choice, annealing temperature and cycle number. Our results show that DNA degradation does indeed bias community analyses. However, the effect of this bias is of minor importance compared to that induced by changes in PCR conditions. Analyses of the macro and microbiome from passively collected arthropod samples are thus well worth pursuing.

Moment and maximum likelihood estimators for Weibull distributions under length- and area-biased sampling

Science.gov (United States)

Jeffrey H. Gove

2003-01-01

Many of the most popular sampling schemes used in forestry are probability proportional to size methods. These methods are also referred to as size biased because sampling is actually from a weighted form of the underlying population distribution. Length- and area-biased sampling are special cases of size-biased sampling where the probability weighting comes from a...

HICOSMO - cosmology with a complete sample of galaxy clusters - I. Data analysis, sample selection and luminosity-mass scaling relation

Science.gov (United States)

Schellenberger, G.; Reiprich, T. H.

2017-08-01

The X-ray regime, where the most massive visible component of galaxy clusters, the intracluster medium, is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to characterize these objects. The aim of this project is to analyse a complete sample of galaxy clusters in detail and constrain cosmological parameters, like the matter density, Ωm, or the amplitude of initial density fluctuations, σ8. The purely X-ray flux-limited sample (HIFLUGCS) consists of the 64 X-ray brightest galaxy clusters, which are excellent targets to study the systematic effects, that can bias results. We analysed in total 196 Chandra observations of the 64 HIFLUGCS clusters, with a total exposure time of 7.7 Ms. Here, we present our data analysis procedure (including an automated substructure detection and an energy band optimization for surface brightness profile analysis) that gives individually determined, robust total mass estimates. These masses are tested against dynamical and Planck Sunyaev-Zeldovich (SZ) derived masses of the same clusters, where good overall agreement is found with the dynamical masses. The Planck SZ masses seem to show a mass-dependent bias to our hydrostatic masses; possible biases in this mass-mass comparison are discussed including the Planck selection function. Furthermore, we show the results for the (0.1-2.4) keV luminosity versus mass scaling relation. The overall slope of the sample (1.34) is in agreement with expectations and values from literature. Splitting the sample into galaxy groups and clusters reveals, even after a selection bias correction, that galaxy groups exhibit a significantly steeper slope (1.88) compared to clusters (1.06).

Sampling Realistic Protein Conformations Using Local Structural Bias

DEFF Research Database (Denmark)

Hamelryck, Thomas Wim; Kent, John T.; Krogh, A.

2006-01-01

The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to attack the problem: a conformational sampling method generates plausible candidate structures, which...... are subsequently accepted or rejected using an energy function. Conceptually, this often corresponds to separating local structural bias from the long-range interactions that stabilize the compact, native state. However, sampling protein conformations that are compatible with the local structural bias encoded...... in a given protein sequence is a long-standing open problem, especially in continuous space. We describe an elegant and mathematically rigorous method to do this, and show that it readily generates native-like protein conformations simply by enforcing compactness. Our results have far-reaching implications...

Field-based random sampling without a sampling frame: control selection for a case-control study in rural Africa.

Science.gov (United States)

Crampin, A C; Mwinuka, V; Malema, S S; Glynn, J R; Fine, P E

2001-01-01

Selection bias, particularly of controls, is common in case-control studies and may materially affect the results. Methods of control selection should be tailored both for the risk factors and disease under investigation and for the population being studied. We present here a control selection method devised for a case-control study of tuberculosis in rural Africa (Karonga, northern Malawi) that selects an age/sex frequency-matched random sample of the population, with a geographical distribution in proportion to the population density. We also present an audit of the selection process, and discuss the potential of this method in other settings.

Selective information sampling

Directory of Open Access Journals (Sweden)

Peter A. F. Fraser-Mackenzie

2009-06-01

Full Text Available This study investigates the amount and valence of information selected during single item evaluation. One hundred and thirty-five participants evaluated a cell phone by reading hypothetical customers reports. Some participants were first asked to provide a preliminary rating based on a picture of the phone and some technical specifications. The participants who were given the customer reports only after they made a preliminary rating exhibited valence bias in their selection of customers reports. In contrast, the participants that did not make an initial rating sought subsequent information in a more balanced, albeit still selective, manner. The preliminary raters used the least amount of information in their final decision, resulting in faster decision times. The study appears to support the notion that selective exposure is utilized in order to develop cognitive coherence.

Assessing total nitrogen in surface-water samples--precision and bias of analytical and computational methods

Science.gov (United States)

Rus, David L.; Patton, Charles J.; Mueller, David K.; Crawford, Charles G.

2013-01-01

concentrations up to 750 mg/L (median of -4.4 percent). This lent support to the laboratory-experiment finding that some particulate nitrogen is sequestered during the digestion process, and demonstrated that negative TN-A bias was present in samples with very low suspended-sediment concentrations. At sediment concentrations above 750 mg/L, the negative TN-A bias became more likely and larger (median of -13.2 percent), suggesting a secondary mechanism of bias, such as reagent limitation. From a geospatial perspective, trends in TN-A bias were not explained by selected basin characteristics. Though variable, TN-K bias generally was positive in the synoptic-field study (median of 3.1 percent), probably as a result of the reduction of nitrate. Three alternative approaches for assessing TN in surface water were evaluated for their impacts on existing and future sampling programs. Replacing TN-A with TN-C would remove the bias from subsequent data, but this approach also would introduce discontinuity in historical records. Replacing TN-K with TN-C would lead to the removal of positive bias in TN-K in the presence of elevated nitrate. However, in addition to the issues that may arise from a discontinuity in the data record, this approach may not be applicable to regulatory programs that require the use of total Kjeldahl nitrogen for stream assessment. By adding TN-C to existing TN-A or TN-K analyses, historical-data continuity would be preserved and the transitional period could be used to minimize the impact of bias on data analyses. This approach, however, imposes the greatest burdens on field operations and in terms of analytical costs. The variation in these impacts on different sampling programs will challenge U.S. Geological Survey scientists attempting to establish uniform standards for TN sample collection and analytical determinations.

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

Directory of Open Access Journals (Sweden)

Nicolas Heslot

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

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

Science.gov (United States)

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

2013-01-01

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

Bespoke Bias for Obtaining Free Energy Differences within Variationally Enhanced Sampling.

Science.gov (United States)

McCarty, James; Valsson, Omar; Parrinello, Michele

2016-05-10

Obtaining efficient sampling of multiple metastable states through molecular dynamics and hence determining free energy differences is central for understanding many important phenomena. Here we present a new biasing strategy, which employs the recent variationally enhanced sampling approach (Valsson and Parrinello Phys. Rev. Lett. 2014, 113, 090601). The bias is constructed from an intuitive model of the local free energy surface describing fluctuations around metastable minima and depends on only a few parameters which are determined variationally such that efficient sampling between states is obtained. The bias constructed in this manner largely reduces the need of finding a set of collective variables that completely spans the conformational space of interest, as they only need to be a locally valid descriptor of the system about its local minimum. We introduce the method and demonstrate its power on two representative examples.

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

Directory of Open Access Journals (Sweden)

Serafini Maria

2003-11-01

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

Selection Bias in Educational Transition Models: Theory and Empirical Evidence

DEFF Research Database (Denmark)

Holm, Anders; Jæger, Mads

variables. This paper, first, explains theoretically how selection on unobserved variables leads to waning coefficients and, second, illustrates empirically how selection leads to biased estimates of the effect of family background on educational transitions. Our empirical analysis using data from...

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

Science.gov (United States)

Wolbring, Tobias; Treischl, Edgar

2016-01-01

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

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

Assessing the joint effect of population stratification and sample selection in studies of gene-gene (environment interactions

Directory of Open Access Journals (Sweden)

Cheng KF

2012-01-01

Full Text Available Abstract Background It is well known that the presence of population stratification (PS may cause the usual test in case-control studies to produce spurious gene-disease associations. However, the impact of the PS and sample selection (SS is less known. In this paper, we provide a systematic study of the joint effect of PS and SS under a more general risk model containing genetic and environmental factors. We provide simulation results to show the magnitude of the bias and its impact on type I error rate of the usual chi-square test under a wide range of PS level and selection bias. Results The biases to the estimation of main and interaction effect are quantified and then their bounds derived. The estimated bounds can be used to compute conservative p-values for the association test. If the conservative p-value is smaller than the significance level, we can safely claim that the association test is significant regardless of the presence of PS or not, or if there is any selection bias. We also identify conditions for the null bias. The bias depends on the allele frequencies, exposure rates, gene-environment odds ratios and disease risks across subpopulations and the sampling of the cases and controls. Conclusion Our results show that the bias cannot be ignored even the case and control data were matched in ethnicity. A real example is given to illustrate application of the conservative p-value. These results are useful to the genetic association studies of main and interaction effects.

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

Directory of Open Access Journals (Sweden)

Rochelle Yang

2017-07-01

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

Role Appropriateness of Educational Fields: Bias in Selection.

Science.gov (United States)

Smith, Elizabeth P.; And Others

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

The perils of straying from protocol: sampling bias and interviewer effects.

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Carrie J Ngongo

Full Text Available Fidelity to research protocol is critical. In a contingent valuation study in an informal urban settlement in Nairobi, Kenya, participants responded differently to the three trained interviewers. Interviewer effects were present during the survey pilot, then magnified at the start of the main survey after a seemingly slight adaptation of the survey sampling protocol allowed interviewers to speak with the "closest neighbor" in the event that no one was home at a selected household. This slight degree of interviewer choice led to inferred sampling bias. Multinomial logistic regression and post-estimation tests revealed that the three interviewers' samples differed significantly from one another according to six demographic characteristics. The two female interviewers were 2.8 and 7.7 times less likely to talk with respondents of low socio-economic status than the male interviewer. Systematic error renders it impossible to determine which of the survey responses might be "correct." This experience demonstrates why researchers must take care to strictly follow sampling protocols, consistently train interviewers, and monitor responses by interview to ensure similarity between interviewers' groups and produce unbiased estimates of the parameters of interest.

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.

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.

Estimating Sampling Biases and Measurement Uncertainties of AIRS-AMSU-A Temperature and Water Vapor Observations Using MERRA Reanalysis

Science.gov (United States)

Hearty, Thomas J.; Savtchenko, Andrey K.; Tian, Baijun; Fetzer, Eric; Yung, Yuk L.; Theobald, Michael; Vollmer, Bruce; Fishbein, Evan; Won, Young-In

2014-01-01

We use MERRA (Modern Era Retrospective-Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU-A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU-A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be +/- 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and greater than 30% dry over mid-latitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU-A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations.

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

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

Science.gov (United States)

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

2011-09-01

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

Effect of sample size on bias correction performance

Science.gov (United States)

Reiter, Philipp; Gutjahr, Oliver; Schefczyk, Lukas; Heinemann, Günther; Casper, Markus C.

2014-05-01

The output of climate models often shows a bias when compared to observed data, so that a preprocessing is necessary before using it as climate forcing in impact modeling (e.g. hydrology, species distribution). A common bias correction method is the quantile matching approach, which adapts the cumulative distribution function of the model output to the one of the observed data by means of a transfer function. Especially for precipitation we expect the bias correction performance to strongly depend on sample size, i.e. the length of the period used for calibration of the transfer function. We carry out experiments using the precipitation output of ten regional climate model (RCM) hindcast runs from the EU-ENSEMBLES project and the E-OBS observational dataset for the period 1961 to 2000. The 40 years are split into a 30 year calibration period and a 10 year validation period. In the first step, for each RCM transfer functions are set up cell-by-cell, using the complete 30 year calibration period. The derived transfer functions are applied to the validation period of the respective RCM precipitation output and the mean absolute errors in reference to the observational dataset are calculated. These values are treated as "best fit" for the respective RCM. In the next step, this procedure is redone using subperiods out of the 30 year calibration period. The lengths of these subperiods are reduced from 29 years down to a minimum of 1 year, only considering subperiods of consecutive years. This leads to an increasing number of repetitions for smaller sample sizes (e.g. 2 for a length of 29 years). In the last step, the mean absolute errors are statistically tested against the "best fit" of the respective RCM to compare the performances. In order to analyze if the intensity of the effect of sample size depends on the chosen correction method, four variations of the quantile matching approach (PTF, QUANT/eQM, gQM, GQM) are applied in this study. The experiments are further

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

Science.gov (United States)

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

2011-09-12

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

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

OpenAIRE

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

2011-01-01

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

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

Directory of Open Access Journals (Sweden)

Daping Zhao

2014-01-01

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

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

Directory of Open Access Journals (Sweden)

Mistry Pramod K

2011-09-01

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

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...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models.......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...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...

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.

When POS datasets don’t add up: Combatting sample bias

DEFF Research Database (Denmark)

Hovy, Dirk; Plank, Barbara; Søgaard, Anders

2014-01-01

Several works in Natural Language Processing have recently looked into part-of-speech (POS) annotation of Twitter data and typically used their own data sets. Since conventions on Twitter change rapidly, models often show sample bias. Training on a combination of the existing data sets should help...... overcome this bias and produce more robust models than any trained on the individual corpora. Unfortunately, combining the existing corpora proves difficult: many of the corpora use proprietary tag sets that have little or no overlap. Even when mapped to a common tag set, the different corpora...

A two-phase sampling survey for nonresponse and its paradata to correct nonresponse bias in a health surveillance survey.

Science.gov (United States)

Santin, G; Bénézet, L; Geoffroy-Perez, B; Bouyer, J; Guéguen, A

2017-02-01

The decline in participation rates in surveys, including epidemiological surveillance surveys, has become a real concern since it may increase nonresponse bias. The aim of this study is to estimate the contribution of a complementary survey among a subsample of nonrespondents, and the additional contribution of paradata in correcting for nonresponse bias in an occupational health surveillance survey. In 2010, 10,000 workers were randomly selected and sent a postal questionnaire. Sociodemographic data were available for the whole sample. After data collection of the questionnaires, a complementary survey among a random subsample of 500 nonrespondents was performed using a questionnaire administered by an interviewer. Paradata were collected for the complete subsample of the complementary survey. Nonresponse bias in the initial sample and in the combined samples were assessed using variables from administrative databases available for the whole sample, not subject to differential measurement errors. Corrected prevalences by reweighting technique were estimated by first using the initial survey alone and then the initial and complementary surveys combined, under several assumptions regarding the missing data process. Results were compared by computing relative errors. The response rates of the initial and complementary surveys were 23.6% and 62.6%, respectively. For the initial and the combined surveys, the relative errors decreased after correction for nonresponse on sociodemographic variables. For the combined surveys without paradata, relative errors decreased compared with the initial survey. The contribution of the paradata was weak. When a complex descriptive survey has a low response rate, a short complementary survey among nonrespondents with a protocol which aims to maximize the response rates, is useful. The contribution of sociodemographic variables in correcting for nonresponse bias is important whereas the additional contribution of paradata in

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

OpenAIRE

Preciado, Daniel; Munneke, Jaap; Theeuwes, Jan

2017-01-01

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

Effects of state and trait anxiety on selective attention to threatening stimuli in a non-clinical sample of school children

Directory of Open Access Journals (Sweden)

Jeniffer Ortega Marín

2015-01-01

Full Text Available Attentional biases, consisting of a preferential processing of threatening stimuli, have been found in anxious adults as predicted by several cognitive models. However, studies with non-clinical samples of children have provided mixed results. therefore, the aim of this research was to determine the effects of state and trait anxiety on the selective attention towards threatening stimuli in a non-clinical sample of school children (age: 8 to 13, n = 110 using the dot-probe task. This study did not reveal an effect of trait anxiety on selective attention towards threatening stimuli. However, a significant difference was found between participants with low state anxiety and high state anxiety. Nevertheless, the effect size was small. Specifically, participants with low state anxiety showed a bias towards threatening stimuli. Overall, the findings of this research with a non-clinical sample of school children suggest that attentional biases towards threatening information, which has been repeatedly found in anxious adults, are not necessarily inherent to non-clinical anxiety in children and on the other hand, the relationship between attentional biases and anxiety in this population might be moderated by other cognitive processes.

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...

Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

Science.gov (United States)

Khazaal, Yasser; van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-07-07

The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.

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

Addressing sampling bias in counting forest birds: a West African ...

African Journals Online (AJOL)

Addressing sampling bias in counting forest birds: a West African case study. ... result may occur because of the noise they may introduce into the analysis. ... used at all; and for all transects to reach their mid-point at the same time each day, ...

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias.

Science.gov (United States)

Hugo, Sanet; Altwegg, Res

2017-09-01

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5' × 5' grid cell, or "pentad"). The explanatory variables were distance to major road and exceptional birding locations or "sampling hubs," percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.

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.

Active and Adaptive Learning from Biased Data with Applications in Astronomy

DEFF Research Database (Denmark)

Kremer, Jan

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

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.

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.

Health indicators: eliminating bias from convenience sampling estimators.

Science.gov (United States)

Hedt, Bethany L; Pagano, Marcello

2011-02-28

Public health practitioners are often called upon to make inference about a health indicator for a population at large when the sole available information are data gathered from a convenience sample, such as data gathered on visitors to a clinic. These data may be of the highest quality and quite extensive, but the biases inherent in a convenience sample preclude the legitimate use of powerful inferential tools that are usually associated with a random sample. In general, we know nothing about those who do not visit the clinic beyond the fact that they do not visit the clinic. An alternative is to take a random sample of the population. However, we show that this solution would be wasteful if it excluded the use of available information. Hence, we present a simple annealing methodology that combines a relatively small, and presumably far less expensive, random sample with the convenience sample. This allows us to not only take advantage of powerful inferential tools, but also provides more accurate information than that available from just using data from the random sample alone. Copyright © 2011 John Wiley & Sons, Ltd.

Sniffing out the Secret Poison: Selection Bias in Educational Research

Science.gov (United States)

Showalter, Daniel A.; Mullet, Luke B.

2017-01-01

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

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

Science.gov (United States)

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

2016-01-01

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

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

NARCIS (Netherlands)

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

2003-01-01

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

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

Science.gov (United States)

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

Simulating publication bias

DEFF Research Database (Denmark)

Paldam, Martin

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

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.

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

Science.gov (United States)

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

2015-03-01

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

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.

Disentangling the effects of selection and loss bias on gene dynamics

NARCIS (Netherlands)

Iranzo, J.; Cuesta, J.A.; Manrubia, S.; Katsnelson, M.I.; Koonin, E.V.

2017-01-01

We combine mathematical modeling of genome evolution with comparative analysis of prokaryotic genomes to estimate the relative contributions of selection and intrinsic loss bias to the evolution of different functional classes of genes and mobile genetic elements (MGE). An exact solution for the

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

Science.gov (United States)

Berglund, Lars

2012-08-01

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

Evaluation of pump pulsation in respirable size-selective sampling: part II. Changes in sampling efficiency.

Science.gov (United States)

Lee, Eun Gyung; Lee, Taekhee; Kim, Seung Won; Lee, Larry; Flemmer, Michael M; Harper, Martin

2014-01-01

This second, and concluding, part of this study evaluated changes in sampling efficiency of respirable size-selective samplers due to air pulsations generated by the selected personal sampling pumps characterized in Part I (Lee E, Lee L, Möhlmann C et al. Evaluation of pump pulsation in respirable size-selective sampling: Part I. Pulsation measurements. Ann Occup Hyg 2013). Nine particle sizes of monodisperse ammonium fluorescein (from 1 to 9 μm mass median aerodynamic diameter) were generated individually by a vibrating orifice aerosol generator from dilute solutions of fluorescein in aqueous ammonia and then injected into an environmental chamber. To collect these particles, 10-mm nylon cyclones, also known as Dorr-Oliver (DO) cyclones, were used with five medium volumetric flow rate pumps. Those were the Apex IS, HFS513, GilAir5, Elite5, and Basic5 pumps, which were found in Part I to generate pulsations of 5% (the lowest), 25%, 30%, 56%, and 70% (the highest), respectively. GK2.69 cyclones were used with the Legacy [pump pulsation (PP) = 15%] and Elite12 (PP = 41%) pumps for collection at high flows. The DO cyclone was also used to evaluate changes in sampling efficiency due to pulse shape. The HFS513 pump, which generates a more complex pulse shape, was compared to a single sine wave fluctuation generated by a piston. The luminescent intensity of the fluorescein extracted from each sample was measured with a luminescence spectrometer. Sampling efficiencies were obtained by dividing the intensity of the fluorescein extracted from the filter placed in a cyclone with the intensity obtained from the filter used with a sharp-edged reference sampler. Then, sampling efficiency curves were generated using a sigmoid function with three parameters and each sampling efficiency curve was compared to that of the reference cyclone by constructing bias maps. In general, no change in sampling efficiency (bias under ±10%) was observed until pulsations exceeded 25% for the

Comparison of Relative Bias, Precision, and Efficiency of Sampling Methods for Natural Enemies of Soybean Aphid (Hemiptera: Aphididae).

Science.gov (United States)

Bannerman, J A; Costamagna, A C; McCornack, B P; Ragsdale, D W

2015-06-01

Generalist natural enemies play an important role in controlling soybean aphid, Aphis glycines (Hemiptera: Aphididae), in North America. Several sampling methods are used to monitor natural enemy populations in soybean, but there has been little work investigating their relative bias, precision, and efficiency. We compare five sampling methods: quadrats, whole-plant counts, sweep-netting, walking transects, and yellow sticky cards to determine the most practical methods for sampling the three most prominent species, which included Harmonia axyridis (Pallas), Coccinella septempunctata L. (Coleoptera: Coccinellidae), and Orius insidiosus (Say) (Hemiptera: Anthocoridae). We show an important time by sampling method interaction indicated by diverging community similarities within and between sampling methods as the growing season progressed. Similarly, correlations between sampling methods for the three most abundant species over multiple time periods indicated differences in relative bias between sampling methods and suggests that bias is not consistent throughout the growing season, particularly for sticky cards and whole-plant samples. Furthermore, we show that sticky cards produce strongly biased capture rates relative to the other four sampling methods. Precision and efficiency differed between sampling methods and sticky cards produced the most precise (but highly biased) results for adult natural enemies, while walking transects and whole-plant counts were the most efficient methods for detecting coccinellids and O. insidiosus, respectively. Based on bias, precision, and efficiency considerations, the most practical sampling methods for monitoring in soybean include walking transects for coccinellid detection and whole-plant counts for detection of small predators like O. insidiosus. Sweep-netting and quadrat samples are also useful for some applications, when efficiency is not paramount. © The Authors 2015. Published by Oxford University Press on behalf of

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

Rovno Amber Ant Assamblage: Bias toward Arboreal Strata or Sampling Effect?

Directory of Open Access Journals (Sweden)

Perkovsky E. E.

2016-06-01

Full Text Available In 2015 B. Guenard with co-authors indicated that the Rovno amber ant assemblage, as described by G. Dlussky and A. Rasnitsyn (2009, showed modest support for a bias towards arboreal origin comparing the Baltic and Bitterfeld assemblages, although it is not clear whether this reflects a sampling error or a signal of real deviation. Since 2009, the Rovno ant collection has now grown more than twice in volume which makes possible to check if the above inference about the essentially arboreal character of the assemblage is real or due to a sampling error. The comparison provided suggests in favour of the latter reason for the bias revealed by B. Guenard and co-authors. The new and larger data on the Rovno assemblage show that the share of non-arboreal ants is now well comparable with those concerning the Baltic and Bitterfeld assemblages. This holds true for the both total assemblages and subassemblages of worker ants only.

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.

SURVIVAL ANALYSIS AND LENGTH-BIASED SAMPLING

Directory of Open Access Journals (Sweden)

Masoud Asgharian

2010-12-01

Full Text Available When survival data are colleted as part of a prevalent cohort study, the recruited cases have already experienced their initiating event. These prevalent cases are then followed for a fixed period of time at the end of which the subjects will either have failed or have been censored. When interests lies in estimating the survival distribution, from onset, of subjects with the disease, one must take into account that the survival times of the cases in a prevalent cohort study are left truncated. When it is possible to assume that there has not been any epidemic of the disease over the past period of time that covers the onset times of the subjects, one may assume that the underlying incidence process that generates the initiating event times is a stationary Poisson process. Under such assumption, the survival times of the recruited subjects are called “lengthbiased”. I discuss the challenges one is faced with in analyzing these type of data. To address the theoretical aspects of the work, I present asymptotic results for the NPMLE of the length-biased as well as the unbiased survival distribution. I also discuss estimating the unbiased survival function using only the follow-up time. This addresses the case that the onset times are either unknown or known with uncertainty. Some of our most recent work and open questions will be presented. These include some aspects of analysis of covariates, strong approximation, functional LIL and density estimation under length-biased sampling with right censoring. The results will be illustrated with survival data from patients with dementia, collected as part of the Canadian Study of Health and Aging (CSHA.

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

Science.gov (United States)

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

2014-01-01

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

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

Science.gov (United States)

Hanley, James A

2017-04-01

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

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

DEFF Research Database (Denmark)

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

2009-01-01

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

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

NARCIS (Netherlands)

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

2012-01-01

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

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.

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

Science.gov (United States)

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

2014-01-01

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

A sequential sampling account of response bias and speed-accuracy tradeoffs in a conflict detection task.

Science.gov (United States)

Vuckovic, Anita; Kwantes, Peter J; Humphreys, Michael; Neal, Andrew

2014-03-01

Signal Detection Theory (SDT; Green & Swets, 1966) is a popular tool for understanding decision making. However, it does not account for the time taken to make a decision, nor why response bias might change over time. Sequential sampling models provide a way of accounting for speed-accuracy trade-offs and response bias shifts. In this study, we test the validity of a sequential sampling model of conflict detection in a simulated air traffic control task by assessing whether two of its key parameters respond to experimental manipulations in a theoretically consistent way. Through experimental instructions, we manipulated participants' response bias and the relative speed or accuracy of their responses. The sequential sampling model was able to replicate the trends in the conflict responses as well as response time across all conditions. Consistent with our predictions, manipulating response bias was associated primarily with changes in the model's Criterion parameter, whereas manipulating speed-accuracy instructions was associated with changes in the Threshold parameter. The success of the model in replicating the human data suggests we can use the parameters of the model to gain an insight into the underlying response bias and speed-accuracy preferences common to dynamic decision-making tasks. © 2013 American Psychological Association

Publication Bias in Psychology: A Diagnosis Based on the Correlation between Effect Size and Sample Size

Science.gov (United States)

Kühberger, Anton; Fritz, Astrid; Scherndl, Thomas

2014-01-01

Background The p value obtained from a significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias. Methods We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values. Results We found a negative correlation of r = −.45 [95% CI: −.53; −.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings. Conclusion The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology. PMID:25192357

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

Science.gov (United States)

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

2016-10-01

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 microevolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have causal effects on fitness, but few valid tests of this exist. Here, we show, using a quantitative genetic framework and six decades of life-history data on two free-living populations of great tits Parus major, that selection estimates for egg-laying date and clutch size are relatively unbiased. Predicted responses to selection based on the Robertson-Price Identity were similar to those based on the multivariate breeder's equation (MVBE), indicating that unmeasured covarying traits were not missing from the analysis. Changing patterns of phenotypic selection on these traits (for laying date, linked to climate change) therefore reflect changing selection on breeding values, and genetic constraints appear not to limit their independent evolution. Quantitative genetic analysis of correlational data from pedigreed populations can be a valuable complement to experimental approaches to help identify whether apparent associations between traits and fitness are biased by missing traits, and to parse the roles of direct versus indirect selection across a range of environments. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Characteristics of bias-based harassment incidents reported by a national sample of U.S. adolescents.

Science.gov (United States)

Jones, Lisa M; Mitchell, Kimberly J; Turner, Heather A; Ybarra, Michele L

2018-06-01

Using a national sample of youth from the U.S., this paper examines incidents of bias-based harassment by peers that include language about victims' perceived sexual orientation, race/ethnicity, religion, weight or height, or intelligence. Telephone interviews were conducted with youth who were 10-20 years old (n = 791). One in six youth (17%) reported at least one experience with bias-based harassment in the past year. Bias language was a part of over half (52%) of all harassment incidents experienced by youth. Perpetrators of bias-based harassment were similar demographically to perpetrators of non-biased harassment. However, bias-based incidents were more likely to involve multiple perpetrators, longer timeframes and multiple harassment episodes. Even controlling for these related characteristics, the use of bias language in incidents of peer harassment resulted in significantly greater odds that youth felt sad as a result of the victimization, skipped school, avoided school activities, and lost friends, compared to non-biased harassment incidents. Copyright © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Randomized controlled trial of attention bias modification in a racially diverse, socially anxious, alcohol dependent sample.

Science.gov (United States)

Clerkin, Elise M; Magee, Joshua C; Wells, Tony T; Beard, Courtney; Barnett, Nancy P

2016-12-01

Attention biases may be an important treatment target for both alcohol dependence and social anxiety. This is the first ABM trial to investigate two (vs. one) targets of attention bias within a sample with co-occurring symptoms of social anxiety and alcohol dependence. Additionally, we used trial-level bias scores (TL-BS) to capture the phenomena of attention bias in a more ecologically valid, dynamic way compared to traditional attention bias scores. Adult participants (N = 86; 41% Female; 52% African American; 40% White) with elevated social anxiety symptoms and alcohol dependence were randomly assigned to an 8-session training condition in this 2 (Social Anxiety ABM vs. Social Anxiety Control) by 2 (Alcohol ABM vs. Alcohol Control) design. Symptoms of social anxiety, alcohol dependence, and attention bias were assessed across time. Multilevel models estimated the trajectories for each measure within individuals, and tested whether these trajectories differed according to the randomized training conditions. Across time, there were significant or trending decreases in all attention TL-BS parameters (but not traditional attention bias scores) and most symptom measures. However, there were not significant differences in the trajectories of change between any ABM and control conditions for any symptom measures. These findings add to previous evidence questioning the robustness of ABM and point to the need to extend the effects of ABM to samples that are racially diverse and/or have co-occurring psychopathology. The results also illustrate the potential importance of calculating trial-level attention bias scores rather than only including traditional bias scores. Copyright © 2016 Elsevier Ltd. All rights reserved.

Randomized Controlled Trial of Attention Bias Modification in a Racially Diverse, Socially Anxious, Alcohol Dependent Sample

Science.gov (United States)

Clerkin, Elise M.; Magee, Joshua C.; Wells, Tony T.; Beard, Courtney; Barnett, Nancy P.

2016-01-01

Objective Attention biases may be an important treatment target for both alcohol dependence and social anxiety. This is the first ABM trial to investigate two (vs. one) targets of attention bias within a sample with co-occurring symptoms of social anxiety and alcohol dependence. Additionally, we used trial-level bias scores (TL-BS) to capture the phenomena of attention bias in a more ecologically valid, dynamic way compared to traditional attention bias scores. Method Adult participants (N=86; 41% Female; 52% African American; 40% White) with elevated social anxiety symptoms and alcohol dependence were randomly assigned to an 8-session training condition in this 2 (Social Anxiety ABM vs. Social Anxiety Control) by 2 (Alcohol ABM vs. Alcohol Control) design. Symptoms of social anxiety, alcohol dependence, and attention bias were assessed across time. Results Multilevel models estimated the trajectories for each measure within individuals, and tested whether these trajectories differed according to the randomized training conditions. Across time, there were significant or trending decreases in all attention TL-BS parameters (but not traditional attention bias scores) and most symptom measures. However, there were not significant differences in the trajectories of change between any ABM and control conditions for any symptom measures. Conclusions These findings add to previous evidence questioning the robustness of ABM and point to the need to extend the effects of ABM to samples that are racially diverse and/or have co-occurring psychopathology. The results also illustrate the potential importance of calculating trial-level attention bias scores rather than only including traditional bias scores. PMID:27591918

Media Bias and Advertising: Evidence from German Car Magazines

OpenAIRE

Dewenter, Ralf; Heimeshoff, Ulrich

2011-01-01

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

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.

Introducing etch kernels for efficient pattern sampling and etch bias prediction

Science.gov (United States)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels, as well as the choice of calibration patterns, is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels-"internal, external, curvature, Gaussian, z_profile"-designed to represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.

Bias in segmented gamma scans arising from size differences between calibration standards and assay samples

International Nuclear Information System (INIS)

Sampson, T.E.

1991-01-01

Recent advances in segmented gamma scanning have emphasized software corrections for gamma-ray self-adsorption in particulates or lumps of special nuclear material in the sample. another feature of this software is an attenuation correction factor formalism that explicitly accounts for differences in sample container size and composition between the calibration standards and the individual items being measured. Software without this container-size correction produces biases when the unknowns are not packaged in the same containers as the calibration standards. This new software allows the use of different size and composition containers for standards and unknowns, as enormous savings considering the expense of multiple calibration standard sets otherwise needed. This paper presents calculations of the bias resulting from not using this new formalism. These calculations may be used to estimate bias corrections for segmented gamma scanners that do not incorporate these advanced concepts

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

International Nuclear Information System (INIS)

Albers, D.J.; Hripcsak, George

2012-01-01

Highlights: ► Time-delayed mutual information for irregularly sampled time-series. ► Estimation bias for the time-delayed mutual information calculation. ► Fast, simple, PDF estimator independent, time-delayed mutual information bias estimate. ► Quantification of data-set-size limits of the time-delayed mutual calculation. - Abstract: A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database.

Personality, Attentional Biases towards Emotional Faces and Symptoms of Mental Disorders in an Adolescent Sample.

Science.gov (United States)

O'Leary-Barrett, Maeve; Pihl, Robert O; Artiges, Eric; Banaschewski, Tobias; Bokde, Arun L W; Büchel, Christian; Flor, Herta; Frouin, Vincent; Garavan, Hugh; Heinz, Andreas; Ittermann, Bernd; Mann, Karl; Paillère-Martinot, Marie-Laure; Nees, Frauke; Paus, Tomas; Pausova, Zdenka; Poustka, Luise; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Ströhle, Andreas; Schumann, Gunter; Conrod, Patricia J

2015-01-01

To investigate the role of personality factors and attentional biases towards emotional faces, in establishing concurrent and prospective risk for mental disorder diagnosis in adolescence. Data were obtained as part of the IMAGEN study, conducted across 8 European sites, with a community sample of 2257 adolescents. At 14 years, participants completed an emotional variant of the dot-probe task, as well two personality measures, namely the Substance Use Risk Profile Scale and the revised NEO Personality Inventory. At 14 and 16 years, participants and their parents were interviewed to determine symptoms of mental disorders. Personality traits were general and specific risk indicators for mental disorders at 14 years. Increased specificity was obtained when investigating the likelihood of mental disorders over a 2-year period, with the Substance Use Risk Profile Scale showing incremental validity over the NEO Personality Inventory. Attentional biases to emotional faces did not characterise or predict mental disorders examined in the current sample. Personality traits can indicate concurrent and prospective risk for mental disorders in a community youth sample, and identify at-risk youth beyond the impact of baseline symptoms. This study does not support the hypothesis that attentional biases mediate the relationship between personality and psychopathology in a community sample. Task and sample characteristics that contribute to differing results among studies are discussed.

Addressing small sample size bias in multiple-biomarker trials: Inclusion of biomarker-negative patients and Firth correction.

Science.gov (United States)

Habermehl, Christina; Benner, Axel; Kopp-Schneider, Annette

2018-03-01

In recent years, numerous approaches for biomarker-based clinical trials have been developed. One of these developments are multiple-biomarker trials, which aim to investigate multiple biomarkers simultaneously in independent subtrials. For low-prevalence biomarkers, small sample sizes within the subtrials have to be expected, as well as many biomarker-negative patients at the screening stage. The small sample sizes may make it unfeasible to analyze the subtrials individually. This imposes the need to develop new approaches for the analysis of such trials. With an expected large group of biomarker-negative patients, it seems reasonable to explore options to benefit from including them in such trials. We consider advantages and disadvantages of the inclusion of biomarker-negative patients in a multiple-biomarker trial with a survival endpoint. We discuss design options that include biomarker-negative patients in the study and address the issue of small sample size bias in such trials. We carry out a simulation study for a design where biomarker-negative patients are kept in the study and are treated with standard of care. We compare three different analysis approaches based on the Cox model to examine if the inclusion of biomarker-negative patients can provide a benefit with respect to bias and variance of the treatment effect estimates. We apply the Firth correction to reduce the small sample size bias. The results of the simulation study suggest that for small sample situations, the Firth correction should be applied to adjust for the small sample size bias. Additional to the Firth penalty, the inclusion of biomarker-negative patients in the analysis can lead to further but small improvements in bias and standard deviation of the estimates. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bias of shear wave elasticity measurements in thin layer samples and a simple correction strategy.

Science.gov (United States)

Mo, Jianqiang; Xu, Hao; Qiang, Bo; Giambini, Hugo; Kinnick, Randall; An, Kai-Nan; Chen, Shigao; Luo, Zongping

2016-01-01

Shear wave elastography (SWE) is an emerging technique for measuring biological tissue stiffness. However, the application of SWE in thin layer tissues is limited by bias due to the influence of geometry on measured shear wave speed. In this study, we investigated the bias of Young's modulus measured by SWE in thin layer gelatin-agar phantoms, and compared the result with finite element method and Lamb wave model simulation. The result indicated that the Young's modulus measured by SWE decreased continuously when the sample thickness decreased, and this effect was more significant for smaller thickness. We proposed a new empirical formula which can conveniently correct the bias without the need of using complicated mathematical modeling. In summary, we confirmed the nonlinear relation between thickness and Young's modulus measured by SWE in thin layer samples, and offered a simple and practical correction strategy which is convenient for clinicians to use.

A Study of Assimilation Bias in Name-Based Sampling of Migrants

Directory of Open Access Journals (Sweden)

Schnell Rainer

2014-06-01

Full Text Available The use of personal names for screening is an increasingly popular sampling technique for migrant populations. Although this is often an effective sampling procedure, very little is known about the properties of this method. Based on a large German survey, this article compares characteristics of respondents whose names have been correctly classified as belonging to a migrant population with respondentswho aremigrants and whose names have not been classified as belonging to a migrant population. Although significant differences were found for some variables even with some large effect sizes, the overall bias introduced by name-based sampling (NBS is small as long as procedures with small false-negative rates are employed.

A "Scientific Diversity" Intervention to Reduce Gender Bias in a Sample of Life Scientists.

Science.gov (United States)

Moss-Racusin, Corinne A; van der Toorn, Jojanneke; Dovidio, John F; Brescoll, Victoria L; Graham, Mark J; Handelsman, Jo

2016-01-01

Mounting experimental evidence suggests that subtle gender biases favoring men contribute to the underrepresentation of women in science, technology, engineering, and mathematics (STEM), including many subfields of the life sciences. However, there are relatively few evaluations of diversity interventions designed to reduce gender biases within the STEM community. Because gender biases distort the meritocratic evaluation and advancement of students, interventions targeting instructors' biases are particularly needed. We evaluated one such intervention, a workshop called "Scientific Diversity" that was consistent with an established framework guiding the development of diversity interventions designed to reduce biases and was administered to a sample of life science instructors (N = 126) at several sessions of the National Academies Summer Institute for Undergraduate Education held nationwide. Evidence emerged indicating the efficacy of the "Scientific Diversity" workshop, such that participants were more aware of gender bias, expressed less gender bias, and were more willing to engage in actions to reduce gender bias 2 weeks after participating in the intervention compared with 2 weeks before the intervention. Implications for diversity interventions aimed at reducing gender bias and broadening the participation of women in the life sciences are discussed. © 2016 C. A. Moss-Racusin et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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.

Some Insights into Analytical Bias Involved in the Application of Grab Sampling for Volatile Organic Compounds: A Case Study against Used Tedlar Bags

Science.gov (United States)

Ghosh, Samik; Kim, Ki-Hyun; Sohn, Jong Ryeul

2011-01-01

In this study, we have examined the patterns of VOCs released from used Tedlar bags that were once used for the collection under strong source activities. In this way, we attempted to account for the possible bias associated with the repetitive use of Tedlar bags. To this end, we selected the bags that were never heated. All of these target bags were used in ambient temperature (typically at or below 30°C). These bags were also dealt carefully to avoid any mechanical abrasion. This study will provide the essential information regarding the interaction between VOCs and Tedlar bag materials as a potential source of bias in bag sampling approaches. PMID:22235175

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.

Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis

Science.gov (United States)

Miyazaki, Kazuyuki; Bowman, Kevin

2017-07-01

The Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) ensemble ozone simulations for the present day from the 2000 decade simulation results are evaluated by a state-of-the-art multi-constituent atmospheric chemical reanalysis that ingests multiple satellite data including the Tropospheric Emission Spectrometer (TES), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Measurement of Pollution in the Troposphere (MOPITT) for 2005-2009. Validation of the chemical reanalysis against global ozonesondes shows good agreement throughout the free troposphere and lower stratosphere for both seasonal and year-to-year variations, with an annual mean bias of less than 0.9 ppb in the middle and upper troposphere at the tropics and mid-latitudes. The reanalysis provides comprehensive spatiotemporal evaluation of chemistry-model performance that compliments direct ozonesonde comparisons, which are shown to suffer from significant sampling bias. The reanalysis reveals that the ACCMIP ensemble mean overestimates ozone in the northern extratropics by 6-11 ppb while underestimating by up to 18 ppb in the southern tropics over the Atlantic in the lower troposphere. Most models underestimate the spatial variability of the annual mean lower tropospheric concentrations in the extratropics of both hemispheres by up to 70 %. The ensemble mean also overestimates the seasonal amplitude by 25-70 % in the northern extratropics and overestimates the inter-hemispheric gradient by about 30 % in the lower and middle troposphere. A part of the discrepancies can be attributed to the 5-year reanalysis data for the decadal model simulations. However, these differences are less evident with the current sonde network. To estimate ozonesonde sampling biases, we computed model bias separately for global coverage and the ozonesonde network. The ozonesonde sampling bias in the evaluated model bias for the seasonal mean concentration relative to global

Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

Science.gov (United States)

Ning, Jing; Chen, Yong; Piao, Jin

2017-07-01

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Technical note: Alternatives to reduce adipose tissue sampling bias.

Science.gov (United States)

Cruz, G D; Wang, Y; Fadel, J G

2014-10-01

Understanding the mechanisms by which nutritional and pharmaceutical factors can manipulate adipose tissue growth and development in production animals has direct and indirect effects in the profitability of an enterprise. Adipocyte cellularity (number and size) is a key biological response that is commonly measured in animal science research. The variability and sampling of adipocyte cellularity within a muscle has been addressed in previous studies, but no attempt to critically investigate these issues has been proposed in the literature. The present study evaluated 2 sampling techniques (random and systematic) in an attempt to minimize sampling bias and to determine the minimum number of samples from 1 to 15 needed to represent the overall adipose tissue in the muscle. Both sampling procedures were applied on adipose tissue samples dissected from 30 longissimus muscles from cattle finished either on grass or grain. Briefly, adipose tissue samples were fixed with osmium tetroxide, and size and number of adipocytes were determined by a Coulter Counter. These results were then fit in a finite mixture model to obtain distribution parameters of each sample. To evaluate the benefits of increasing number of samples and the advantage of the new sampling technique, the concept of acceptance ratio was used; simply stated, the higher the acceptance ratio, the better the representation of the overall population. As expected, a great improvement on the estimation of the overall adipocyte cellularity parameters was observed using both sampling techniques when sample size number increased from 1 to 15 samples, considering both techniques' acceptance ratio increased from approximately 3 to 25%. When comparing sampling techniques, the systematic procedure slightly improved parameters estimation. The results suggest that more detailed research using other sampling techniques may provide better estimates for minimum sampling.

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.

Correction of sampling bias in a cross-sectional study of post-surgical complications.

Science.gov (United States)

Fluss, Ronen; Mandel, Micha; Freedman, Laurence S; Weiss, Inbal Salz; Zohar, Anat Ekka; Haklai, Ziona; Gordon, Ethel-Sherry; Simchen, Elisheva

2013-06-30

Cross-sectional designs are often used to monitor the proportion of infections and other post-surgical complications acquired in hospitals. However, conventional methods for estimating incidence proportions when applied to cross-sectional data may provide estimators that are highly biased, as cross-sectional designs tend to include a high proportion of patients with prolonged hospitalization. One common solution is to use sampling weights in the analysis, which adjust for the sampling bias inherent in a cross-sectional design. The current paper describes in detail a method to build weights for a national survey of post-surgical complications conducted in Israel. We use the weights to estimate the probability of surgical site infections following colon resection, and validate the results of the weighted analysis by comparing them with those obtained from a parallel study with a historically prospective design. Copyright © 2012 John Wiley & Sons, Ltd.

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

OpenAIRE

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

2005-01-01

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

An experimental investigation of recruitment bias in eating pathology research.

Science.gov (United States)

Moss, Erin L; von Ranson, Kristin M

2006-04-01

Previous, uncontrolled research has suggested a bias may exist in recruiting participants for eating disorder research. Recruitment biases may affect sample representativeness and generalizability of findings. This experiment investigated whether revealing that a study's topic was related to eating disorders created a self-selection bias. Young women at a university responded to advertisements containing contrasting information about the nature of a single study. We recruited one group by advertising the study under the title "Disordered Eating in Young Women" (n = 251) and another group using the title "Consumer Preferences" (n = 259). Results indicated similar levels of eating pathology in both groups, so the different recruitment techniques did not engender self-selection. However, the consumer preferences group scored higher in self-reported social desirability. The level of information conveyed in study advertising does not impact reporting of eating disturbances among nonclinical samples, although there is evidence social desirability might. 2006 by Wiley Periodicals, Inc.

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

Science.gov (United States)

Nelson, Jon P

2014-01-01

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

Implicit and Explicit Weight Bias in a National Sample of 4732 Medical Students: The Medical Student CHANGES Study

OpenAIRE

Phelan, Sean M.; Dovidio, John F.; Puhl, Rebecca M.; Burgess, Diana J.; Nelson, David B.; Yeazel, Mark W.; Hardeman, Rachel; Perry, Sylvia; van Ryn, Michelle

2014-01-01

Objective To examine the magnitude of explicit and implicit weight biases compared to biases against other groups; and identify student factors predicting bias in a large national sample of medical students. Design and Methods A web-based survey was completed by 4732 1st year medical students from 49 medical schools as part of a longitudinal study of medical education. The survey included a validated measure of implicit weight bias, the implicit association test, and 2 measures of explicit bi...

40 CFR 89.507 - Sample selection.

Science.gov (United States)

2010-07-01

... Auditing § 89.507 Sample selection. (a) Engines comprising a test sample will be selected at the location...). However, once the manufacturer ships any test engine, it relinquishes the prerogative to conduct retests...

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

Science.gov (United States)

Berthet, Pierre; Lansner, Anders

2014-01-01

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.

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

Science.gov (United States)

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

2018-03-07

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

40 CFR 90.507 - Sample selection.

Science.gov (United States)

2010-07-01

... Auditing § 90.507 Sample selection. (a) Engines comprising a test sample will be selected at the location... manufacturer ships any test engine, it relinquishes the prerogative to conduct retests as provided in § 90.508...

A novel approach to non-biased systematic random sampling: a stereologic estimate of Purkinje cells in the human cerebellum.

Science.gov (United States)

Agashiwala, Rajiv M; Louis, Elan D; Hof, Patrick R; Perl, Daniel P

2008-10-21

Non-biased systematic sampling using the principles of stereology provides accurate quantitative estimates of objects within neuroanatomic structures. However, the basic principles of stereology are not optimally suited for counting objects that selectively exist within a limited but complex and convoluted portion of the sample, such as occurs when counting cerebellar Purkinje cells. In an effort to quantify Purkinje cells in association with certain neurodegenerative disorders, we developed a new method for stereologic sampling of the cerebellar cortex, involving calculating the volume of the cerebellar tissues, identifying and isolating the Purkinje cell layer and using this information to extrapolate non-biased systematic sampling data to estimate the total number of Purkinje cells in the tissues. Using this approach, we counted Purkinje cells in the right cerebella of four human male control specimens, aged 41, 67, 70 and 84 years, and estimated the total Purkinje cell number for the four entire cerebella to be 27.03, 19.74, 20.44 and 22.03 million cells, respectively. The precision of the method is seen when comparing the density of the cells within the tissue: 266,274, 173,166, 167,603 and 183,575 cells/cm3, respectively. Prior literature documents Purkinje cell counts ranging from 14.8 to 30.5 million cells. These data demonstrate the accuracy of our approach. Our novel approach, which offers an improvement over previous methodologies, is of value for quantitative work of this nature. This approach could be applied to morphometric studies of other similarly complex tissues as well.

Estimation bias and bias correction in reduced rank autoregressions

DEFF Research Database (Denmark)

Nielsen, Heino Bohn

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Boulesteix Anne-Laure

2009-12-01

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

Sample Selection for Training Cascade Detectors

OpenAIRE

V?llez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our a...

The impact of non-response bias due to sampling in public health studies: A comparison of voluntary versus mandatory recruitment in a Dutch national survey on adolescent health.

Science.gov (United States)

Cheung, Kei Long; Ten Klooster, Peter M; Smit, Cees; de Vries, Hein; Pieterse, Marcel E

2017-03-23

In public health monitoring of young people it is critical to understand the effects of selective non-response, in particular when a controversial topic is involved like substance abuse or sexual behaviour. Research that is dependent upon voluntary subject participation is particularly vulnerable to sampling bias. As respondents whose participation is hardest to elicit on a voluntary basis are also more likely to report risk behaviour, this potentially leads to underestimation of risk factor prevalence. Inviting adolescents to participate in a home-sent postal survey is a typical voluntary recruitment strategy with high non-response, as opposed to mandatory participation during school time. This study examines the extent to which prevalence estimates of adolescent health-related characteristics are biased due to different sampling methods, and whether this also biases within-subject analyses. Cross-sectional datasets collected in 2011 in Twente and IJsselland, two similar and adjacent regions in the Netherlands, were used. In total, 9360 youngsters in a mandatory sample (Twente) and 1952 youngsters in a voluntary sample (IJsselland) participated in the study. To test whether the samples differed on health-related variables, we conducted both univariate and multivariable logistic regression analyses controlling for any demographic difference between the samples. Additional multivariable logistic regressions were conducted to examine moderating effects of sampling method on associations between health-related variables. As expected, females, older individuals, as well as individuals with higher education levels, were over-represented in the voluntary sample, compared to the mandatory sample. Respondents in the voluntary sample tended to smoke less, consume less alcohol (ever, lifetime, and past four weeks), have better mental health, have better subjective health status, have more positive school experiences and have less sexual intercourse than respondents in the

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

NARCIS (Netherlands)

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

2012-01-01

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

Search strategy using LHC pileup interactions as a zero bias sample

Science.gov (United States)

Nachman, Benjamin; Rubbo, Francesco

2018-05-01

Due to a limited bandwidth and a large proton-proton interaction cross section relative to the rate of interesting physics processes, most events produced at the Large Hadron Collider (LHC) are discarded in real time. A sophisticated trigger system must quickly decide which events should be kept and is very efficient for a broad range of processes. However, there are many processes that cannot be accommodated by this trigger system. Furthermore, there may be models of physics beyond the standard model (BSM) constructed after data taking that could have been triggered, but no trigger was implemented at run time. Both of these cases can be covered by exploiting pileup interactions as an effective zero bias sample. At the end of high-luminosity LHC operations, this zero bias dataset will have accumulated about 1 fb-1 of data from which a bottom line cross section limit of O (1 ) fb can be set for BSM models already in the literature and those yet to come.

Biased representation of disturbance rates in the roadside sampling frame in boreal forests: implications for monitoring design

Directory of Open Access Journals (Sweden)

Steven L. Van Wilgenburg

2015-12-01

Full Text Available The North American Breeding Bird Survey (BBS is the principal source of data to inform researchers about the status of and trend for boreal forest birds. Unfortunately, little BBS coverage is available in the boreal forest, where increasing concern over the status of species breeding there has increased interest in northward expansion of the BBS. However, high disturbance rates in the boreal forest may complicate roadside monitoring. If the roadside sampling frame does not capture variation in disturbance rates because of either road placement or the use of roads for resource extraction, biased trend estimates might result. In this study, we examined roadside bias in the proportional representation of habitat disturbance via spatial data on forest "loss," forest fires, and anthropogenic disturbance. In each of 455 BBS routes, the area disturbed within multiple buffers away from the road was calculated and compared against the area disturbed in degree blocks and BBS strata. We found a nonlinear relationship between bias and distance from the road, suggesting forest loss and forest fires were underrepresented below 75 and 100 m, respectively. In contrast, anthropogenic disturbance was overrepresented at distances below 500 m and underrepresented thereafter. After accounting for distance from road, BBS routes were reasonably representative of the degree blocks they were within, with only a few strata showing biased representation. In general, anthropogenic disturbance is overrepresented in southern strata, and forest fires are underrepresented in almost all strata. Similar biases exist when comparing the entire road network and the subset sampled by BBS routes against the amount of disturbance within BBS strata; however, the magnitude of biases differed. Based on our results, we recommend that spatial stratification and rotating panel designs be used to spread limited BBS and off-road sampling effort in an unbiased fashion and that new BBS routes

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

Uninformative polymorphisms bias genome scans for signatures of selection

Directory of Open Access Journals (Sweden)

Roesti Marius

2012-06-01

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

Pathogen prevalence, group bias, and collectivism in the standard cross-cultural sample.

Science.gov (United States)

Cashdan, Elizabeth; Steele, Matthew

2013-03-01

It has been argued that people in areas with high pathogen loads will be more likely to avoid outsiders, to be biased in favor of in-groups, and to hold collectivist and conformist values. Cross-national studies have supported these predictions. In this paper we provide new pathogen codes for the 186 cultures of the Standard Cross-Cultural Sample and use them, together with existing pathogen and ethnographic data, to try to replicate these cross-national findings. In support of the theory, we found that cultures in high pathogen areas were more likely to socialize children toward collectivist values (obedience rather than self-reliance). There was some evidence that pathogens were associated with reduced adult dispersal. However, we found no evidence of an association between pathogens and our measures of group bias (in-group loyalty and xenophobia) or intergroup contact.

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.

Examinations of Home Economics Textbooks for Sex Bias.

Science.gov (United States)

Weis, Susan F.

1979-01-01

Four analyses were conducted on a sample of 100 randomly selected, secondary home economics textbooks published between 1964 and 1974. Results indicated that the contents presented sex bias in language usage, in pictures portraying male and female role environments, and in role behaviors and expectations emphasized. (Author/JH)

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

Science.gov (United States)

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

2013-01-01

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

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

Directory of Open Access Journals (Sweden)

J. Van der Heyden

2016-08-01

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

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

International Nuclear Information System (INIS)

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

2008-01-01

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

Analysing home-ownership of couples: the effect of selecting couples at the time of the survey.

Science.gov (United States)

Mulder, C H

1996-09-01

"The analysis of events encountered by couple and family households may suffer from sample selection bias when data are restricted to couples existing at the moment of interview. The paper discusses the effect of sample selection bias on event history analyses of buying a home [in the Netherlands] by comparing analyses performed on a sample of existing couples with analyses of a more complete sample including past as well as current partner relationships. The results show that, although home-buying in relationships that have ended differs clearly from behaviour in existing relationships, sample selection bias is not alarmingly large." (SUMMARY IN FRE) excerpt

Design-based estimators for snowball sampling

OpenAIRE

Shafie, Termeh

2010-01-01

Snowball sampling, where existing study subjects recruit further subjects from amongtheir acquaintances, is a popular approach when sampling from hidden populations.Since people with many in-links are more likely to be selected, there will be a selectionbias in the samples obtained. In order to eliminate this bias, the sample data must beweighted. However, the exact selection probabilities are unknown for snowball samplesand need to be approximated in an appropriate way. This paper proposes d...

Attentional Bias towards Positive Emotion Predicts Stress Resilience.

Science.gov (United States)

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

2016-01-01

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

Survey Response-Related Biases in Contingent Valuation: Concepts, Remedies, and Empirical Application to Valuing Aquatic Plant Management

Science.gov (United States)

Mark L. Messonnier; John C. Bergstrom; Chrisopher M. Cornwell; R. Jeff Teasley; H. Ken Cordell

2000-01-01

Simple nonresponse and selection biases that may occur in survey research such as contingent valuation applications are discussed and tested. Correction mechanisms for these types of biases are demonstrated. Results indicate the importance of testing and correcting for unit and item nonresponse bias in contingent valuation survey data. When sample nonresponse and...

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

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

Science.gov (United States)

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

2007-09-15

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

How to investigate and adjust for selection bias in cohort studies

DEFF Research Database (Denmark)

Nohr, Ellen A; Liew, Zeyan

2018-01-01

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

An Approach to Addressing Selection Bias in Survival Analysis

Science.gov (United States)

Carlin, Caroline S.; Solid, Craig A.

2014-01-01

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

Measurement of the Tracer Gradient and Sampling System Bias of the Hot Fuel Examination Facility Stack Air Monitoring System

Energy Technology Data Exchange (ETDEWEB)

Glissmeyer, John A.; Flaherty, Julia E.

2011-07-20

This report describes tracer gas uniformity and bias measurements made in the exhaust air discharge of the Hot Fuel Examination Facility at Idaho National Laboratory. The measurements were a follow-up on earlier measurements which indicated a lack of mixing of the two ventilation streams being discharged via a common stack. The lack of mixing is detrimental to the accuracy of air emission measurements. The lack of mixing was confirmed in these new measurements. The air sampling probe was found to be out of alignment and that was corrected. The suspected sampling bias in the air sample stream was disproved.

A method of language sampling

DEFF Research Database (Denmark)

Rijkhoff, Jan; Bakker, Dik; Hengeveld, Kees

1993-01-01

In recent years more attention is paid to the quality of language samples in typological work. Without an adequate sampling strategy, samples may suffer from various kinds of bias. In this article we propose a sampling method in which the genetic criterion is taken as the most important: samples...... to determine how many languages from each phylum should be selected, given any required sample size....

Species richness in soil bacterial communities: a proposed approach to overcome sample size bias.

Science.gov (United States)

Youssef, Noha H; Elshahed, Mostafa S

2008-09-01

Estimates of species richness based on 16S rRNA gene clone libraries are increasingly utilized to gauge the level of bacterial diversity within various ecosystems. However, previous studies have indicated that regardless of the utilized approach, species richness estimates obtained are dependent on the size of the analyzed clone libraries. We here propose an approach to overcome sample size bias in species richness estimates in complex microbial communities. Parametric (Maximum likelihood-based and rarefaction curve-based) and non-parametric approaches were used to estimate species richness in a library of 13,001 near full-length 16S rRNA clones derived from soil, as well as in multiple subsets of the original library. Species richness estimates obtained increased with the increase in library size. To obtain a sample size-unbiased estimate of species richness, we calculated the theoretical clone library sizes required to encounter the estimated species richness at various clone library sizes, used curve fitting to determine the theoretical clone library size required to encounter the "true" species richness, and subsequently determined the corresponding sample size-unbiased species richness value. Using this approach, sample size-unbiased estimates of 17,230, 15,571, and 33,912 were obtained for the ML-based, rarefaction curve-based, and ACE-1 estimators, respectively, compared to bias-uncorrected values of 15,009, 11,913, and 20,909.

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

Science.gov (United States)

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

2014-11-21

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

Multiclass gene selection using Pareto-fronts.

Science.gov (United States)

Rajapakse, Jagath C; Mundra, Piyushkumar A

2013-01-01

Filter methods are often used for selection of genes in multiclass sample classification by using microarray data. Such techniques usually tend to bias toward a few classes that are easily distinguishable from other classes due to imbalances of strong features and sample sizes of different classes. It could therefore lead to selection of redundant genes while missing the relevant genes, leading to poor classification of tissue samples. In this manuscript, we propose to decompose multiclass ranking statistics into class-specific statistics and then use Pareto-front analysis for selection of genes. This alleviates the bias induced by class intrinsic characteristics of dominating classes. The use of Pareto-front analysis is demonstrated on two filter criteria commonly used for gene selection: F-score and KW-score. A significant improvement in classification performance and reduction in redundancy among top-ranked genes were achieved in experiments with both synthetic and real-benchmark data sets.

Investigation of bias in a study of nuclear shipyard workers

International Nuclear Information System (INIS)

Greenberg, E.R.; Rosner, B.; Hennekens, C.; Rinsky, R.; Colton, T.

1985-01-01

The authors examined discrepant findings between a 1978 proportional mortality study and a 1981 cohort study of workers at the Portsmouth, New Hampshire Naval Shipyard to determine whether the healthy worker effect, selection bias, or measurement bias could explain why only the proportional mortality study found excess cancer deaths among nuclear workers. Lower mortality from noncancer causes in nuclear workers (the healthy worker effect) partly accounted for the observed elevated cancer proportional mortality. More important, however, was measurement bias which occurred in the proportional mortality study when nuclear workers who had not died of cancer were misclassified as not being nuclear workers based on information from their next of kin, thereby, creating a spurious association. Although the proportional mortality study was based on a small sample of all deaths occuring in the cohort, selection bias did not contribute materially to the discrepant results for total cancer deaths. With regard to leukemia, misclassification of occupation in the proportional mortality study and disagreement about cause of death accounted for some of the reported excess deaths. 16 references, 4 tables

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...

UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

Directory of Open Access Journals (Sweden)

A. Kianisarkaleh

2015-12-01

Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

Lepidosaurian diversity in the Mesozoic-Palaeogene: the potential roles of sampling biases and environmental drivers

Science.gov (United States)

Cleary, Terri J.; Benson, Roger B. J.; Evans, Susan E.; Barrett, Paul M.

2018-03-01

Lepidosauria is a speciose clade with a long evolutionary history, but there have been few attempts to explore its taxon richness through time. Here we estimate patterns of terrestrial lepidosaur genus diversity for the Triassic-Palaeogene (252-23 Ma), and compare observed and sampling-corrected richness curves generated using Shareholder Quorum Subsampling and classical rarefaction. Generalized least-squares regression (GLS) is used to investigate the relationships between richness, sampling and environmental proxies. We found low levels of richness from the Triassic until the Late Cretaceous (except in the Kimmeridgian-Tithonian of Europe). High richness is recovered for the Late Cretaceous of North America, which declined across the K-Pg boundary but remained relatively high throughout the Palaeogene. Richness decreased following the Eocene-Oligocene Grande Coupure in North America and Europe, but remained high in North America and very high in Europe compared to the Late Cretaceous; elsewhere data are lacking. GLS analyses indicate that sampling biases (particularly, the number of fossil collections per interval) are the best explanation for long-term face-value genus richness trends. The lepidosaur fossil record presents many problems when attempting to reconstruct past diversity, with geographical sampling biases being of particular concern, especially in the Southern Hemisphere.

Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration

Directory of Open Access Journals (Sweden)

Cameron Abrams

2013-12-01

Full Text Available We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In collective-variable biasing, we first discuss methods stemming from thermodynamic integration that use mean force biasing, including the adaptive biasing force algorithm and temperature acceleration. We then turn to methods that use bias potentials, including umbrella sampling and metadynamics. We next consider parallel tempering and replica-exchange methods. We conclude with a brief presentation of some combination methods.

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

Science.gov (United States)

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

2018-07-12

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

EVIDENCE FOR (AND AGAINST) PROGENITOR BIAS IN THE SIZE GROWTH OF COMPACT RED GALAXIES

Energy Technology Data Exchange (ETDEWEB)

Keating, Stephanie K.; Abraham, Roberto G. [Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4 (Canada); Schiavon, Ricardo [Gemini Observatory, 670 North A' ohoku Place, Hilo, HI 96720 (United States); Graves, Genevieve [Department of Astronomy, University of California, Berkeley, CA 94720 (United States); Damjanov, Ivana [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Yan, Renbin [Department of Physics and Astronomy, University of Kentucky, 505 Rose Street, Lexington, KY 40506-0055 (United States); Newman, Jeffrey [Department of Physics and Astronomy, University of Pittsburgh, 3941 O' Hara Street, Pittsburgh, PA 15260 (United States); Simard, Luc [National Research Council of Canada, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada)

2015-01-01

Most massive, passive galaxies are compact at high redshifts, but similarly compact massive galaxies are rare in the local universe. The most common interpretation of this phenomenon is that massive galaxies have grown in size by a factor of about five since redshift z = 2. An alternative explanation is that recently quenched massive galaxies are larger (a {sup p}rogenitor bias{sup )}. In this paper, we explore the importance of progenitor bias by looking for systematic differences in the stellar populations of compact early-type galaxies in the DEEP2 survey as a function of size. Our analysis is based on applying the statistical technique of bootstrap resampling to constrain differences in the median ages of our samples and to begin to characterize the distribution of stellar populations in our co-added spectra. The light-weighted ages of compact early-type galaxies at redshifts 0.5 < z < 1.4 are compared to those of a control sample of larger galaxies at similar redshifts. We find that massive compact early-type galaxies selected on the basis of red color and high bulge-to-total ratio are younger than similarly selected larger galaxies, suggesting that size growth in these objects is not driven mainly by progenitor bias, and that individual galaxies grow as their stellar populations age. However, compact early-type galaxies selected on the basis of image smoothness and high bulge-to-total ratio are older than a control sample of larger galaxies. Progenitor bias will play a significant role in defining the apparent size changes of early-type galaxies if they are selected on the basis of the smoothness of their light distributions.

Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender.

Directory of Open Access Journals (Sweden)

Janice A Sabin

Full Text Available Overweight patients report weight discrimination in health care settings and subsequent avoidance of routine preventive health care. The purpose of this study was to examine implicit and explicit attitudes about weight among a large group of medical doctors (MDs to determine the pervasiveness of negative attitudes about weight among MDs. Test-takers voluntarily accessed a public Web site, known as Project Implicit®, and opted to complete the Weight Implicit Association Test (IAT (N = 359,261. A sub-sample identified their highest level of education as MD (N = 2,284. Among the MDs, 55% were female, 78% reported their race as white, and 62% had a normal range BMI. This large sample of test-takers showed strong implicit anti-fat bias (Cohen's d = 1.0. MDs, on average, also showed strong implicit anti-fat bias (Cohen's d = 0.93. All test-takers and the MD sub-sample reported a strong preference for thin people rather than fat people or a strong explicit anti-fat bias. We conclude that strong implicit and explicit anti-fat bias is as pervasive among MDs as it is among the general public. An important area for future research is to investigate the association between providers' implicit and explicit attitudes about weight, patient reports of weight discrimination in health care, and quality of care delivered to overweight patients.

Mechanism for and method of biasing magnetic sensor

Science.gov (United States)

Kautz, David R.

2007-12-04

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

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

Science.gov (United States)

Fort, G.; Jourdain, B.; Lelièvre, T.; Stoltz, G.

2018-04-01

We propose a new Monte Carlo method to efficiently sample a multimodal distribution (known up to a normalization constant). We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which can also be seen as a generalization of well-tempered metadynamics. The dynamics is based on an adaptive importance technique. The importance function relies on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to an additive constant, the free-energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang-Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free-energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.

Minimum Bias Trigger in ATLAS

International Nuclear Information System (INIS)

Kwee, Regina

2010-01-01

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

Implicit and Explicit Memory Bias in Opiate Dependent, Abstinent and Normal Individuals

Directory of Open Access Journals (Sweden)

Jafar Hasani

2013-07-01

Full Text Available Objective: The aim of current research was to assess implicit and explicit memory bias to drug related stimuli in opiate Dependent, abstinent and normal Individuals. Method: Three groups including opiate Dependent, abstinent and normal Individuals (n=25 were selected by available sampling method. After matching on the base of age, education level and type of substance use all participants assessed by recognition task (explicit memory bias and stem completion task (implicit memory bias. Results: The analysis of data showed that opiate dependent and abstinent groups in comparison with normal individual had implicit memory bias, whereas in explicit memory only opiate dependent individuals showed bias. Conclusion: The identification of explicit and implicit memory governing addiction may have practical implications in diagnosis, treatment and prevention of substance abuse.

The persistent sampling bias in developmental psychology: A call to action.

Science.gov (United States)

Nielsen, Mark; Haun, Daniel; Kärtner, Joscha; Legare, Cristine H

2017-10-01

Psychology must confront the bias in its broad literature toward the study of participants developing in environments unrepresentative of the vast majority of the world's population. Here, we focus on the implications of addressing this challenge, highlight the need to address overreliance on a narrow participant pool, and emphasize the value and necessity of conducting research with diverse populations. We show that high-impact-factor developmental journals are heavily skewed toward publishing articles with data from WEIRD (Western, educated, industrialized, rich, and democratic) populations. Most critically, despite calls for change and supposed widespread awareness of this problem, there is a habitual dependence on convenience sampling and little evidence that the discipline is making any meaningful movement toward drawing from diverse samples. Failure to confront the possibility that culturally specific findings are being misattributed as universal traits has broad implications for the construction of scientifically defensible theories and for the reliable public dissemination of study findings. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Science.gov (United States)

Hirsch, Robert M.

2014-01-01

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

Communication: Estimating the initial biasing potential for λ-local-elevation umbrella-sampling (λ-LEUS) simulations via slow growth

Energy Technology Data Exchange (ETDEWEB)

Bieler, Noah S.; Hünenberger, Philippe H., E-mail: phil@igc.phys.chem.ethz.ch [Laboratory of Physical Chemistry, ETH Zürich, CH-8093 Zürich (Switzerland)

2014-11-28

In a recent article [Bieler et al., J. Chem. Theory Comput. 10, 3006–3022 (2014)], we introduced a combination of the λ-dynamics (λD) approach for calculating alchemical free-energy differences and of the local-elevation umbrella-sampling (LEUS) memory-based biasing method to enhance the sampling along the alchemical coordinate. The combined scheme, referred to as λ-LEUS, was applied to the perturbation of hydroquinone to benzene in water as a test system, and found to represent an improvement over thermodynamic integration (TI) in terms of sampling efficiency at equivalent accuracy. However, the preoptimization of the biasing potential required in the λ-LEUS method requires “filling up” all the basins in the potential of mean force. This introduces a non-productive pre-sampling time that is system-dependent, and generally exceeds the corresponding equilibration time in a TI calculation. In this letter, a remedy is proposed to this problem, termed the slow growth memory guessing (SGMG) approach. Instead of initializing the biasing potential to zero at the start of the preoptimization, an approximate potential of mean force is estimated from a short slow growth calculation, and its negative used to construct the initial memory. Considering the same test system as in the preceding article, it is shown that of the application of SGMG in λ-LEUS permits to reduce the preoptimization time by about a factor of four.

Communication: Estimating the initial biasing potential for λ-local-elevation umbrella-sampling (λ-LEUS) simulations via slow growth

International Nuclear Information System (INIS)

Bieler, Noah S.; Hünenberger, Philippe H.

2014-01-01

In a recent article [Bieler et al., J. Chem. Theory Comput. 10, 3006–3022 (2014)], we introduced a combination of the λ-dynamics (λD) approach for calculating alchemical free-energy differences and of the local-elevation umbrella-sampling (LEUS) memory-based biasing method to enhance the sampling along the alchemical coordinate. The combined scheme, referred to as λ-LEUS, was applied to the perturbation of hydroquinone to benzene in water as a test system, and found to represent an improvement over thermodynamic integration (TI) in terms of sampling efficiency at equivalent accuracy. However, the preoptimization of the biasing potential required in the λ-LEUS method requires “filling up” all the basins in the potential of mean force. This introduces a non-productive pre-sampling time that is system-dependent, and generally exceeds the corresponding equilibration time in a TI calculation. In this letter, a remedy is proposed to this problem, termed the slow growth memory guessing (SGMG) approach. Instead of initializing the biasing potential to zero at the start of the preoptimization, an approximate potential of mean force is estimated from a short slow growth calculation, and its negative used to construct the initial memory. Considering the same test system as in the preceding article, it is shown that of the application of SGMG in λ-LEUS permits to reduce the preoptimization time by about a factor of four

Approximate Bias Correction in Econometrics

OpenAIRE

James G. MacKinnon; Anthony A. Smith Jr.

1995-01-01

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

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

DEFF Research Database (Denmark)

Bonnichsen, Ole; Olsen, Søren Bøye

2016-01-01

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

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

Science.gov (United States)

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

2016-05-01

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

Biased Brownian dynamics for rate constant calculation.

OpenAIRE

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

2000-01-01

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

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

Science.gov (United States)

Votteler, Andreas; Höner, Oliver

2014-01-01

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

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

Science.gov (United States)

Babbitt, Gregory A; Cotter, C R

2011-01-01

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

Bias modification training can alter approach bias and chocolate consumption.

Science.gov (United States)

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

2016-01-01

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

Forms of Attrition in a Longitudinal Study of Religion and Health in Older Adults and Implications for Sample Bias.

Science.gov (United States)

Hayward, R David; Krause, Neal

2016-02-01

The use of longitudinal designs in the field of religion and health makes it important to understand how attrition bias may affect findings in this area. This study examines attrition in a 4-wave, 8-year study of older adults. Attrition resulted in a sample biased toward more educated and more religiously involved individuals. Conditional linear growth curve models found that trajectories of change for some variables differed among attrition categories. Ineligibles had worsening depression, declining control, and declining attendance. Mortality was associated with worsening religious coping styles. Refusers experienced worsening depression. Nevertheless, there was no evidence of bias in the key religion and health results.

Suspected survivor bias in case-control studies: stratify on survival time and use a negative control.

Science.gov (United States)

van Rein, Nienke; Cannegieter, Suzanne C; Rosendaal, Frits R; Reitsma, Pieter H; Lijfering, Willem M

2014-02-01

Selection bias in case-control studies occurs when control selection is inappropriate. However, selection bias due to improper case sampling is less well recognized. We describe how to recognize survivor bias (i.e., selection on exposed cases) and illustrate this with an example study. A case-control study was used to analyze the effect of statins on major bleedings during treatment with vitamin K antagonists. A total of 110 patients who experienced such bleedings were included 18-1,018 days after the bleeding complication and matched to 220 controls. A protective association of major bleeding for exposure to statins (odds ratio [OR]: 0.56; 95% confidence interval: 0.29-1.08) was found, which did not become stronger after adjustment for confounding factors. These observations lead us to suspect survivor bias. To identify this bias, results were stratified on time between bleeding event and inclusion, and repeated for a negative control (an exposure not related to survival): blood group non-O. The ORs for exposure to statins increased gradually to 1.37 with shorter time between outcome and inclusion, whereas ORs for the negative control remained constant, confirming our hypothesis. We recommend the presented method to check for overoptimistic results, that is, survivor bias in case-control studies. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

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

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

40 CFR 205.171-3 - Test motorcycle sample selection.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Test motorcycle sample selection. 205... ABATEMENT PROGRAMS TRANSPORTATION EQUIPMENT NOISE EMISSION CONTROLS Motorcycle Exhaust Systems § 205.171-3 Test motorcycle sample selection. A test motorcycle to be used for selective enforcement audit testing...

Comparison of Different Sample Preparation Protocols Reveals Lysis Buffer-Specific Extraction Biases in Gram-Negative Bacteria and Human Cells.

Science.gov (United States)

Glatter, Timo; Ahrné, Erik; Schmidt, Alexander

2015-11-06

We evaluated different in-solution and FASP-based sample preparation strategies for absolute protein quantification. Label-free quantification (LFQ) was employed to compare different sample preparation strategies in the bacterium Pseudomonas aeruginosa and human embryonic kidney cells (HEK), and organismal-specific differences in general performance and enrichment of specific protein classes were noted. The original FASP protocol globally enriched for most proteins in the bacterial sample, whereas the sodium deoxycholate in-solution strategy was more efficient with HEK cells. Although detergents were found to be highly suited for global proteome analysis, higher intensities were obtained for high-abundant nucleic acid-associated protein complexes, like the ribosome and histone proteins, using guanidine hydrochloride. Importantly, we show for the first time that the observable total proteome mass of a sample strongly depends on the sample preparation protocol, with some protocols resulting in a significant underestimation of protein mass due to incomplete protein extraction of biased protein groups. Furthermore, we demonstrate that some of the observed abundance biases can be overcome by incorporating a nuclease treatment step or, alternatively, a correction factor for complementary sample preparation approaches.

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

Directory of Open Access Journals (Sweden)

Nicholas Furl

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

A Selective Emotional Decision-Making Bias Elicited by Facial Expressions

Science.gov (United States)

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

2012-01-01

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

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

Science.gov (United States)

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

2012-01-01

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

Racial Bias and Predictive Validity in Testing for Selection.

Science.gov (United States)

1983-07-01

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

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

Science.gov (United States)

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

2009-09-21

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

CEO emotional bias and investment decision, Bayesian network method

Directory of Open Access Journals (Sweden)

Jarboui Anis

2012-08-01

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

THE NONLINEAR BIASING OF THE zCOSMOS GALAXIES UP TO z ∼ 1 FROM THE 10k SAMPLE

International Nuclear Information System (INIS)

Kovac, K.; Porciani, C.; Lilly, S. J.; Oesch, P.; Peng, Y.; Carollo, C. M.; Marinoni, C.; Guzzo, L.; Iovino, A.; Cucciati, O.; Kneib, J.-P.; Le Fevre, O.; Zamorani, G.; Bolzonella, M.; Zucca, E.; Bardelli, S.; Meneux, B.; Contini, T.; Mainieri, V.; Renzini, A.

2011-01-01

We use the zCOSMOS galaxy overdensity field to study the biasing of galaxies in the COSMOS field. By comparing the probability distribution function of the galaxy density contrast δ g to the lognormal approximation of the mass density contrast δ, we obtain the mean biasing function b(δ, z, R) between the galaxy and matter overdensity fields and its second moments b-hat and b-tilde. Over the redshift interval 0.4 g |δ) = b(δ, z, R)δ is of a characteristic shape, requiring nonlinear biasing in the most overdense and underdense regions. Taking into account the uncertainties due to cosmic variance, we do not detect any significant evolution in the (δ g |δ) function, but we do detect a significant redshift evolution in the linear biasing parameter b-hat from 1.23 ± 0.11 at z ∼ 0.55 to 1.62 ± 0.14 at z ∼ 0.75, for a luminosity-complete sample of M B -1 Mpc, but increases systematically with luminosity (at 2σ-3σ significance between the M B B B 12 M sun with a small dependence on the adopted bias-mass relation. Our detailed error analysis and comparison with previous studies lead us to conclude that cosmic variance is the main contributor to the differences in the linear bias measured from different surveys. While our results support the general picture of biased galaxy formation up to z ∼ 1, the fine-tuning of the galaxy formation models is still limited by the restrictions of the current spectroscopic surveys at these redshifts.

Risk Attitudes, Sample Selection and Attrition in a Longitudinal Field Experiment

DEFF Research Database (Denmark)

Harrison, Glenn W.; Lau, Morten Igel

with respect to risk attitudes. Our design builds in explicit randomization on the incentives for participation. We show that there are significant sample selection effects on inferences about the extent of risk aversion, but that the effects of subsequent sample attrition are minimal. Ignoring sample...... selection leads to inferences that subjects in the population are more risk averse than they actually are. Correcting for sample selection and attrition affects utility curvature, but does not affect inferences about probability weighting. Properly accounting for sample selection and attrition effects leads...... to findings of temporal stability in overall risk aversion. However, that stability is around different levels of risk aversion than one might naively infer without the controls for sample selection and attrition we are able to implement. This evidence of “randomization bias” from sample selection...

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…

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.

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

Science.gov (United States)

Cariou, Marie; Duret, Laurent; Charlat, Sylvain

2016-11-08

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

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.

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.

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

Science.gov (United States)

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

2018-02-06

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

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

DEFF Research Database (Denmark)

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

2017-01-01

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

A redshift survey of IRAS galaxies. I. Sample selection

International Nuclear Information System (INIS)

Strauss, M.A.; Davis, M.; Yahil, A.; Huchra, J.P.

1990-01-01

A complete all-sky sample of objects, flux-limited at 60 microns, has been extracted from the data base of the IRAS. The sample consists of 5014 objects, of which 2649 are galaxies and 13 are not yet identified. In order to study large-scale structure with this sample, it must be free of systematic biases. Corrections are applied for a major systematic effect in the flux densities listed in the IRAS Point Source Catalog: sources resolved by the IRAS beam have flux densities systematically underestimated. In addition, accurate flux densities are obtained for sources flagged as variable, or of moderate flux quality at 60 microns. The IRAS detectors suffered radiation-induced responsivity enhancement (hysteresis) due to crossings of the satellite scans across the Galactic plane; this effect is measured and is shown to be negligible. 53 refs

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

Science.gov (United States)

Vetter, Thomas R; Mascha, Edward J

2017-09-01

Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. Random error in an experiment can be due to the natural, periodic fluctuation or variation in the accuracy or precision of virtually any data sampling technique or health measurement tool or scale. In a clinical research study, random error can be due to not only innate human variability but also purely chance. Systematic error in an experiment arises from an innate flaw in the data sampling technique or measurement instrument. In the clinical research setting, systematic error is more commonly referred to as systematic bias. The most commonly encountered types of bias in anesthesia, perioperative, critical care, and pain medicine research include recall bias, observational bias (Hawthorne effect), attrition bias, misclassification or informational bias, and selection bias. A confounding variable is a factor associated with both the exposure of interest and the outcome of interest. A confounding variable (confounding factor or confounder) is a variable that correlates (positively or negatively) with both the exposure and outcome. Confounding is typically not an issue in a randomized trial because the randomized groups are sufficiently balanced on all potential confounding variables, both observed and nonobserved. However, confounding can be a major problem with any observational (nonrandomized) study. Ignoring confounding in an observational study will often result in a "distorted" or incorrect estimate of

Does volumetric absorptive microsampling eliminate the hematocrit bias for caffeine and paraxanthine in dried blood samples? A comparative study.

Science.gov (United States)

De Kesel, Pieter M M; Lambert, Willy E; Stove, Christophe P

2015-06-30

Volumetric absorptive microsampling (VAMS) is a novel sampling technique that allows the straightforward collection of an accurate volume of blood (approximately 10μL) from a drop or pool of blood by dipping an absorbent polymeric tip into it. The resulting blood microsample is dried and analyzed as a whole. The aim of this study was to evaluate the potential of VAMS to overcome the hematocrit bias, an important issue in the analysis of dried blood microsamples. An LC-MS/MS method for analysis of the model compounds caffeine and paraxanthine in VAMS samples was fully validated and fulfilled all pre-established criteria. In conjunction with previously validated procedures for dried blood spots (DBS) and blood, this allowed us to set up a meticulous comparative study in which both compounds were determined in over 80 corresponding VAMS, DBS and liquid whole blood samples. These originated from authentic human patient samples, covering a wide hematocrit range (0.21-0.50). By calculating the differences with reference whole blood concentrations, we found that analyte concentrations in VAMS samples were not affected by a bias that changed over the evaluated hematocrit range, in contrast to DBS results. However, VAMS concentrations tend to overestimate whole blood concentrations, as a consistent positive bias was observed. A different behavior of VAMS samples prepared from incurred and spiked blood, combined with a somewhat reduced recovery of caffeine and paraxanthine from VAMS tips at high hematocrit values, an effect that was not observed for DBS using a very similar extraction procedure, was found to be at the basis of the observed VAMS-whole blood deviations. Based on this study, being the first in which the validity and robustness of VAMS is evaluated by analyzing incurred human samples, it can be concluded that VAMS effectively assists in eliminating the effect of hematocrit. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

A large sample of Kohonen-selected SDSS quasars with weak emission lines: selection effects and statistical properties

Science.gov (United States)

Meusinger, H.; Balafkan, N.

2014-08-01

Aims: A tiny fraction of the quasar population shows remarkably weak emission lines. Several hypotheses have been developed, but the weak line quasar (WLQ) phenomenon still remains puzzling. The aim of this study was to create a sizeable sample of WLQs and WLQ-like objects and to evaluate various properties of this sample. Methods: We performed a search for WLQs in the spectroscopic data from the Sloan Digital Sky Survey Data Release 7 based on Kohonen self-organising maps for nearly 105 quasar spectra. The final sample consists of 365 quasars in the redshift range z = 0.6 - 4.2 (z¯ = 1.50 ± 0.45) and includes in particular a subsample of 46 WLQs with equivalent widths WMg iiattention was paid to selection effects. Results: The WLQs have, on average, significantly higher luminosities, Eddington ratios, and accretion rates. About half of the excess comes from a selection bias, but an intrinsic excess remains probably caused primarily by higher accretion rates. The spectral energy distribution shows a bluer continuum at rest-frame wavelengths ≳1500 Å. The variability in the optical and UV is relatively low, even taking the variability-luminosity anti-correlation into account. The percentage of radio detected quasars and of core-dominant radio sources is significantly higher than for the control sample, whereas the mean radio-loudness is lower. Conclusions: The properties of our WLQ sample can be consistently understood assuming that it consists of a mix of quasars at the beginning of a stage of increased accretion activity and of beamed radio-quiet quasars. The higher luminosities and Eddington ratios in combination with a bluer spectral energy distribution can be explained by hotter continua, i.e. higher accretion rates. If quasar activity consists of subphases with different accretion rates, a change towards a higher rate is probably accompanied by an only slow development of the broad line region. The composite WLQ spectrum can be reasonably matched by the

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

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

Science.gov (United States)

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

2017-12-29

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

Cluster Sampling Bias in Government-Sponsored Evaluations: A Correlational Study of Employment and Welfare Pilots in England.

Science.gov (United States)

Vaganay, Arnaud

2016-01-01

For pilot or experimental employment programme results to apply beyond their test bed, researchers must select 'clusters' (i.e. the job centres delivering the new intervention) that are reasonably representative of the whole territory. More specifically, this requirement must account for conditions that could artificially inflate the effect of a programme, such as the fluidity of the local labour market or the performance of the local job centre. Failure to achieve representativeness results in Cluster Sampling Bias (CSB). This paper makes three contributions to the literature. Theoretically, it approaches the notion of CSB as a human behaviour. It offers a comprehensive theory, whereby researchers with limited resources and conflicting priorities tend to oversample 'effect-enhancing' clusters when piloting a new intervention. Methodologically, it advocates for a 'narrow and deep' scope, as opposed to the 'wide and shallow' scope, which has prevailed so far. The PILOT-2 dataset was developed to test this idea. Empirically, it provides evidence on the prevalence of CSB. In conditions similar to the PILOT-2 case study, investigators (1) do not sample clusters with a view to maximise generalisability; (2) do not oversample 'effect-enhancing' clusters; (3) consistently oversample some clusters, including those with higher-than-average client caseloads; and (4) report their sampling decisions in an inconsistent and generally poor manner. In conclusion, although CSB is prevalent, it is still unclear whether it is intentional and meant to mislead stakeholders about the expected effect of the intervention or due to higher-level constraints or other considerations.

Experimental breakdown of selected anodized aluminum samples in dilute plasmas

Science.gov (United States)

Grier, Norman T.; Domitz, Stanley

1992-01-01

Anodized aluminum samples representative of Space Station Freedom structural material were tested for electrical breakdown under space plasma conditions. In space, this potential arises across the insulating anodized coating when the spacecraft structure is driven to a negative bias relative to the external plasma potential due to plasma-surface interaction phenomena. For anodized materials used in the tests, it was found that breakdown voltage varied from 100 to 2000 volts depending on the sample. The current in the arcs depended on the sample, the capacitor, and the voltage. The level of the arc currents varied from 60 to 1000 amperes. The plasma number density varied from 3 x 10 exp 6 to 10 exp 3 ions per cc. The time between arcs increased as the number density was lowered. Corona testing of anodized samples revealed that samples with higher corona inception voltage had higher arcing inception voltages. From this it is concluded that corona testing may provide a method of screening the samples.

Biomass and abundance biases in European standard gillnet sampling

Czech Academy of Sciences Publication Activity Database

Šmejkal, Marek; Ricard, Daniel; Prchalová, Marie; Říha, Milan; Muška, Milan; Blabolil, Petr; Čech, Martin; Vašek, Mojmír; Jůza, Tomáš; Herreras, A.M.; Encina, L.; Peterka, Jiří; Kubečka, Jan

2015-01-01

Roč. 10, č. 3 (2015), e0122437 E-ISSN 1932-6203 R&D Projects: GA MŠk(CZ) EE2.3.20.0204; GA ČR(CZ) GPP505/12/P647; GA MŠk(CZ) EE2.3.30.0032 Institutional support: RVO:60077344 Keywords : fish sampling * gillnets * large meshes * mesh size selectivity * Improvement of European standard EN 14757 * bream (Abramis brama) Subject RIV: GL - Fishing Impact factor: 3.057, year: 2015

Effect of additional sample bias in Meshed Plasma Immersion Ion Deposition (MPIID) on microstructural, surface and mechanical properties of Si-DLC films

Energy Technology Data Exchange (ETDEWEB)

Wu, Mingzhong [State Key Laboratory of Advanced Welding & Joining, Harbin Institute of Technology, Harbin 150001 (China); School of Materials Science & Engineering, Jiamusi University, Jiamusi 154007 (China); Tian, Xiubo, E-mail: xiubotian@163.com [State Key Laboratory of Advanced Welding & Joining, Harbin Institute of Technology, Harbin 150001 (China); Li, Muqin [School of Materials Science & Engineering, Jiamusi University, Jiamusi 154007 (China); Gong, Chunzhi [State Key Laboratory of Advanced Welding & Joining, Harbin Institute of Technology, Harbin 150001 (China); Wei, Ronghua [Southwest Research Institute, San Antonio, TX 78238 (United States)

2016-07-15

Highlights: • A novel Meshed Plasma Immersion Ion Deposition is proposed. • The deposited Si-DLC films possess denser structures and high deposition rate. • It is attributed to ion bombardment of the deposited films. • The ion energy can be independently controlled by an additional bias (novel set up). - Abstract: Meshed Plasma Immersion Ion Deposition (MPIID) using cage-like hollow cathode discharge is a modified process of conventional PIID, but it allows the deposition of thick diamond-like carbon (DLC) films (up to 50 μm) at a high deposition rate (up to 6.5 μm/h). To further improve the DLC film properties, a new approach to the MPIID process is proposed, in which the energy of ions incident to the sample surface can be independently controlled by an additional voltage applied between the samples and the metal meshed cage. In this study, the meshed cage was biased with a pulsed DC power supply at −1350 V peak voltage for the plasma generation, while the samples inside the cage were biased with a DC voltage from 0 V to −500 V with respect to the cage to study its effect. Si-DLC films were synthesized with a mixture of Ar, C{sub 2}H{sub 2} and tetramethylsilane (TMS). After the depositions, scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray photoelectrons spectroscopy (XPS), Raman spectroscopy and nanoindentation were used to study the morphology, surface roughness, chemical bonding and structure, and the surface hardness as well as the modulus of elasticity of the Si-DLC films. It was observed that the intense ion bombardment significantly densified the films, reduced the surface roughness, reduced the H and Si contents, and increased the nanohardness (H) and modulus of elasticity (E), whereas the deposition rate decreased slightly. Using the H and E data, high values of H{sup 3}/E{sup 2} and H/E were obtained on the biased films, indicating the potential excellent mechanical and tribological properties of the films. In this

40 CFR 205.160-2 - Test sample selection and preparation.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Test sample selection and preparation... sample selection and preparation. (a) Vehicles comprising the sample which are required to be tested... maintained in any manner unless such preparation, tests, modifications, adjustments or maintenance are part...

Final Sampling Bias in Haptic Judgments: How Final Touch Affects Decision-Making.

Science.gov (United States)

Mitsuda, Takashi; Yoshioka, Yuichi

2018-01-01

When people make a choice between multiple items, they usually evaluate each item one after the other repeatedly. The effect of the order and number of evaluating items on one's choices is essential to understanding the decision-making process. Previous studies have shown that when people choose a favorable item from two items, they tend to choose the item that they evaluated last. This tendency has been observed regardless of sensory modalities. This study investigated the origin of this bias by using three experiments involving two-alternative forced-choice tasks using handkerchiefs. First, the bias appeared in a smoothness discrimination task, which indicates that the bias was not based on judgments of preference. Second, the handkerchief that was touched more often tended to be chosen more frequently in the preference task, but not in the smoothness discrimination task, indicating that a mere exposure effect enhanced the bias. Third, in the condition where the number of touches did not differ between handkerchiefs, the bias appeared when people touched a handkerchief they wanted to touch last, but not when people touched the handkerchief that was predetermined. This finding suggests a direct coupling between final voluntary touching and judgment.

The Gender Wage Gap and Sample Selection via Risk Attitudes

OpenAIRE

Jung , Seeun

2014-01-01

This paper investigates a new way to estimate the gender wage gap with the introduction of individual risk attitudes using representative Korean data. We es- timate the wage gap with correction for the selection bias, which latter results in the overestimation of this wage gap. Female workers are more risk averse. They hence prefer working in the public sector, where wages are generally lower than in the private sector. It goes on to explain the reduced gender wage gap by develop- ing an appr...

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

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

Science.gov (United States)

Mogg, Karin; Bradley, Brendan P

2002-12-01

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

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

Science.gov (United States)

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

2016-05-15

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

Good practices for quantitative bias analysis.

Science.gov (United States)

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

2014-12-01

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

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

Corpus-based Transitivity Biases in Individuals with Aphasia

Directory of Open Access Journals (Sweden)

Gayle DeDe

2015-04-01

transitive structures 43.5% of the time and in intransitive structures 14.5% of the time [controls: 50.2% transitive and 9.3% intransitive productions]. The IWA produced intransitively biased verbs in intransitive structures 54.7% of the time and in transitive structures 3.8% of the time [controls: 75.2% intransitive and 4.9% transitive uses]. Next, consider the relative frequency of transitive and intransitive constructions. The IWA used intransitive verbs in 62.4% of their overall productions and transitive verbs in 37.7% of overall productions. In Gahl et al. (2004, control populations produced intransitive verbs in 44.5% of their overall productions and transitive verbs in 55.5% of their productions. Discussion Our results showed that IWA retain typical transitivity and intransitivity biases in their productive uses of a selected sample of verbs. In contrast to controls, IWA produced intransitive structures more often than transitive structures. This trend is consistent with studies showing that IWA tend to produce verbs with simpler argument structures (e.g., Kim & Thompson, 2004. However, our sample of verbs may be more likely to be produced intransitively in response to the AphasiaBank prompts. We are currently coding control data from AphasiaBank, to permit direct comparison of verb usage and types of structures across groups.

Associations between cognitive biases and domains of schizotypy in a non-clinical sample.

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Aldebot Sacks, Stephanie; Weisman de Mamani, Amy Gina; Garcia, Cristina Phoenix

2012-03-30

Schizotypy is a non-clinical manifestation of the same underlying biological factors that give rise to psychotic disorders (Claridge and Beech, 1995). Research on normative populations scoring high on schizotypy is valuable because it may help elucidate the predisposition to schizophrenia (Jahshan and Sergi, 2007) and because performance is not confounded by issues present in schizophrenia samples. In the current study, a Confirmatory Factor Analysis was conducted using several comprehensive measures of schizotypy. As expected and replicating prior research, a four-factor model of schizotypy emerged including a positive, a negative, a cognitive disorganization, and an impulsive nonconformity factor. We also evaluated how each factor related to distinct cognitive biases. In support of hypotheses, increased self-certainty, decreased theory of mind, and decreased source memory were associated with higher scores on the positive factor; decreased theory of mind was associated with higher scores on the negative factor; and increased self-certainty was associated with greater impulsive nonconformity. Unexpectedly, decreased self-certainty and increased theory of mind were associated with greater cognitive disorganization, and decreased source memory was associated with greater impulsive nonconformity. These findings offer new insights by highlighting cognitive biases that may be risk factors for psychosis. Published by Elsevier Ireland Ltd.

A Heckman Selection- t Model

KAUST Repository

Marchenko, Yulia V.; Genton, Marc G.

2012-01-01

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

Mate-sampling costs and sexy sons.

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Kokko, H; Booksmythe, I; Jennions, M D

2015-01-01

Costly female mating preferences for purely Fisherian male traits (i.e. sexual ornaments that are genetically uncorrelated with inherent viability) are not expected to persist at equilibrium. The indirect benefit of producing 'sexy sons' (Fisher process) disappears: in some models, the male trait becomes fixed; in others, a range of male trait values persist, but a larger trait confers no net fitness advantage because it lowers survival. Insufficient indirect selection to counter the direct cost of producing fewer offspring means that preferences are lost. The only well-cited exception assumes biased mutation on male traits. The above findings generally assume constant direct selection against female preferences (i.e. fixed costs). We show that if mate-sampling costs are instead derived based on an explicit account of how females acquire mates, an initially costly mating preference can coevolve with a male trait so that both persist in the presence or absence of biased mutation. Our models predict that empirically detecting selection at equilibrium will be difficult, even if selection was responsible for the location of the current equilibrium. In general, it appears useful to integrate mate sampling theory with models of genetic consequences of mating preferences: being explicit about the process by which individuals select mates can alter equilibria. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Completeness and Bias Tests of Small Flares in gPhoton M Dwarfs

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Fleming, Scott W.; Million, Chase; Brasseur, Clara; Osten, Rachel A.; Shiao, Bernie; Bianchi, Luciana

2018-01-01

gPhoton is a time-tagged database of more than one trillion calibrated ultraviolet photon events from the ten-year GALEX mission. With the open-source gPhoton software, users can construct images and light curves at user-defined temporal and spatial scales. We have been working on a project to detect stellar flares on M dwarfs observed in GALEX data, with particular focus on smaller flares that have durations between 30 seconds and 30 minutes, and energies between 10^27 and 10^29 ergs. This parameter space is still largely unconstrained in the UV/optical, even with Kepler/K2 data, due to their short durations. We present completeness and reliability tests we are conducting to be able to account for selection and detection bias in our sample, including stellar type, instrument artifacts, sampling bias, and signal-to-noise. The bias-corrected occurrence rate of such flares, which are exponentially more frequent than larger flares already characterized by Kepler and ground-based studies, can be included in future calculations of exoplanet habitability.

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

Energy Technology Data Exchange (ETDEWEB)

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

2006-02-01

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

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

Science.gov (United States)

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

2015-05-01

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

A study of total measurement error in tomographic gamma scanning to assay nuclear material with emphasis on a bias issue for low-activity samples

International Nuclear Information System (INIS)

Burr, T.L.; Mercer, D.J.; Prettyman, T.H.

1998-01-01

Field experience with the tomographic gamma scanner to assay nuclear material suggests that the analysis techniques can significantly impact the assay uncertainty. For example, currently implemented image reconstruction methods exhibit a positive bias for low-activity samples. Preliminary studies indicate that bias reduction could be achieved at the expense of increased random error variance. In this paper, the authors examine three possible bias sources: (1) measurement error in the estimated transmission matrix, (2) the positivity constraint on the estimated mass of nuclear material, and (3) improper treatment of the measurement error structure. The authors present results from many small-scale simulation studies to examine this bias/variance tradeoff for a few image reconstruction methods in the presence of the three possible bias sources

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

Directory of Open Access Journals (Sweden)

Zhenglun Pan

2005-12-01

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

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.

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.

Classification based upon gene expression data: bias and precision of error rates.

Science.gov (United States)

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Sampling in epidemiological research: issues, hazards and pitfalls

Science.gov (United States)

Tyrer, Stephen; Heyman, Bob

2016-01-01

Surveys of people's opinions are fraught with difficulties. It is easier to obtain information from those who respond to text messages or to emails than to attempt to obtain a representative sample. Samples of the population that are selected non-randomly in this way are termed convenience samples as they are easy to recruit. This introduces a sampling bias. Such non-probability samples have merit in many situations, but an epidemiological enquiry is of little value unless a random sample is obtained. If a sufficient number of those selected actually complete a survey, the results are likely to be representative of the population. This editorial describes probability and non-probability sampling methods and illustrates the difficulties and suggested solutions in performing accurate epidemiological research. PMID:27087985

Response rates and selection problems, with emphasis on mental health variables and DNA sampling, in large population-based, cross-sectional and longitudinal studies of adolescents in Norway.

Science.gov (United States)

Bjertness, Espen; Sagatun, Ase; Green, Kristian; Lien, Lars; Søgaard, Anne Johanne; Selmer, Randi

2010-10-12

Selection bias is a threat to the internal validity of epidemiological studies. In light of a growing number of studies which aim to provide DNA, as well as a considerable number of invitees who declined to participate, we discuss response rates, predictors of lost to follow-up and failure to provide DNA, and the presence of possible selection bias, based on five samples of adolescents. We included nearly 7,000 adolescents from two longitudinal studies of 18/19 year olds with two corresponding cross-sectional baseline studies at age 15/16 (10th graders), and one cross-sectional study of 13th graders (18/19 years old). DNA was sampled from the cheek mucosa of 18/19 year olds. Predictors of lost to follow-up and failure to provide DNA were studied by Poisson regression. Selection bias in the follow-up at age 18/19 was estimated through investigation of prevalence ratios (PRs) between selected exposures (physical activity, smoking) and outcome variables (general health, mental distress, externalizing problems) measured at baseline. Out of 5,750 who participated at age 15/16, we lost 42% at follow-up at age 18/19. The percentage of participants who gave their consent to DNA provision was as high as the percentage that consented to a linkage of data with other health registers and surveys, approximately 90%. Significant predictors of lost to follow-up and failure to provide DNA samples in the present genetic epidemiological study were: male gender; non-western ethnicity; postal survey compared with school-based; low educational plans; low education and income of father; low perceived family economy; unmarried parents; poor self-reported health; externalized symptoms and smoking, with some differences in subgroups of ethnicity and gender. The association measures (PRs) were quite similar among participants and all invitees, with some minor discrepancies in subgroups of non-western boys and girls. Lost to follow-up had marginal impact on the estimated prevalence ratios

The X-ray luminosity-temperature relation of a complete sample of low-mass galaxy clusters

DEFF Research Database (Denmark)

Zou, S.; Maughan, B. J.; Giles, P. A.

2016-01-01

found for massive clusters to a steeper slope for the lower mass sample studied here. Thanks to our rigorous treatment of selection biases, these measurements provide a robust reference against which to compare predictions of models of the impact of feedback on the X-ray properties of galaxy groups....... (T), taking selection biases fully into account. The logarithmic slope of the bolometric L-T relation was found to be 3.29 ± 0.33, consistent with values typically found for samples of more massive clusters. In combination with other recent studies of the L-T relation, we show...

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

Science.gov (United States)

Ostlund, Sean B; Maidment, Nigel T

2012-01-01

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

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

Science.gov (United States)

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

2015-03-01

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

PeptideManager: A Peptide Selection Tool for Targeted Proteomic Studies Involving Mixed Samples from Different Species

Directory of Open Access Journals (Sweden)

Kevin eDemeure

2014-09-01

Full Text Available The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples.At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human in a host proteome background (e.g., mouse, rat suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze

Negatively-biased credulity and the cultural evolution of beliefs.

Science.gov (United States)

Fessler, Daniel M T; Pisor, Anne C; Navarrete, Carlos David

2014-01-01

The functions of cultural beliefs are often opaque to those who hold them. Accordingly, to benefit from cultural evolution's ability to solve complex adaptive problems, learners must be credulous. However, credulity entails costs, including susceptibility to exploitation, and effort wasted due to false beliefs. One determinant of the optimal level of credulity is the ratio between the costs of two types of errors: erroneous incredulity (failing to believe information that is true) and erroneous credulity (believing information that is false). This ratio can be expected to be asymmetric when information concerns hazards, as the costs of erroneous incredulity will, on average, exceed the costs of erroneous credulity; no equivalent asymmetry characterizes information concerning benefits. Natural selection can therefore be expected to have crafted learners' minds so as to be more credulous toward information concerning hazards. This negatively-biased credulity extends general negativity bias, the adaptive tendency for negative events to be more salient than positive events. Together, these biases constitute attractors that should shape cultural evolution via the aggregated effects of learners' differential retention and transmission of information. In two studies in the U.S., we demonstrate the existence of negatively-biased credulity, and show that it is most pronounced in those who believe the world to be dangerous, individuals who may constitute important nodes in cultural transmission networks. We then document the predicted imbalance in cultural content using a sample of urban legends collected from the Internet and a sample of supernatural beliefs obtained from ethnographies of a representative collection of the world's cultures, showing that beliefs about hazards predominate in both.

Sex Attracts: Investigating Individual Differences in Attentional Bias to Sexual Stimuli

Science.gov (United States)

Kagerer, Sabine; Wehrum, Sina; Klucken, Tim; Walter, Bertram; Vaitl, Dieter; Stark, Rudolf

2014-01-01

We investigated the impact of sexual stimuli and the influence of sexual motivation on the performance in a dot-probe task and a line-orientation task in a large sample of males and females. All pictures (neutral, erotic) were rated on the dimensions of valence, arousal, disgust, and sexual arousal. Additionally, questionnaires measuring sexual interest/desire/motivation were employed. The ratings of the sexual stimuli point to a successful picture selection because sexual arousal did not differ between the sexes. The stimuli were equally arousing for men and women. Higher scores in the employed questionnaires measuring sexual interest/desire/motivation led to higher sexual arousal ratings of the sex pictures. Attentional bias towards sex pictures was observed in both experimental tasks. The attentional biases measured by the dot-probe and the line-orientation task were moderately intercorrelated suggesting attentional bias as a possible marker for a sex-attention trait. Finally, only the sexual sensation seeking score correlated with the attentional biases of the two tasks. Future research is needed to increase the predictive power of these indirect measures of sexual interest. PMID:25238545

Application of bias factor method using random sampling technique for prediction accuracy improvement of critical eigenvalue of BWR

International Nuclear Information System (INIS)

Ito, Motohiro; Endo, Tomohiro; Yamamoto, Akio; Kuroda, Yusuke; Yoshii, Takashi

2017-01-01

The bias factor method based on the random sampling technique is applied to the benchmark problem of Peach Bottom Unit 2. Validity and availability of the present method, i.e. correction of calculation results and reduction of uncertainty, are confirmed in addition to features and performance of the present method. In the present study, core characteristics in cycle 3 are corrected with the proposed method using predicted and 'measured' critical eigenvalues in cycles 1 and 2. As the source of uncertainty, variance-covariance of cross sections is considered. The calculation results indicate that bias between predicted and measured results, and uncertainty owing to cross section can be reduced. Extension to other uncertainties such as thermal hydraulics properties will be a future task. (author)

A method of language sampling

DEFF Research Database (Denmark)

Rijkhoff, Jan; Bakker, Dik; Hengeveld, Kees

1993-01-01

In recent years more attention is paid to the quality of language samples in typological work. Without an adequate sampling strategy, samples may suffer from various kinds of bias. In this article we propose a sampling method in which the genetic criterion is taken as the most important: samples...... created with this method will reflect optimally the diversity of the languages of the world. On the basis of the internal structure of each genetic language tree a measure is computed that reflects the linguistic diversity in the language families represented by these trees. This measure is used...... to determine how many languages from each phylum should be selected, given any required sample size....

A KiDS weak lensing analysis of assembly bias in GAMA galaxy groups

Science.gov (United States)

Dvornik, Andrej; Cacciato, Marcello; Kuijken, Konrad; Viola, Massimo; Hoekstra, Henk; Nakajima, Reiko; van Uitert, Edo; Brouwer, Margot; Choi, Ami; Erben, Thomas; Fenech Conti, Ian; Farrow, Daniel J.; Herbonnet, Ricardo; Heymans, Catherine; Hildebrandt, Hendrik; Hopkins, Andrew M.; McFarland, John; Norberg, Peder; Schneider, Peter; Sifón, Cristóbal; Valentijn, Edwin; Wang, Lingyu

2017-07-01

We investigate possible signatures of halo assembly bias for spectroscopically selected galaxy groups from the Galaxy And Mass Assembly (GAMA) survey using weak lensing measurements from the spatially overlapping regions of the deeper, high-imaging-quality photometric Kilo-Degree Survey. We use GAMA groups with an apparent richness larger than 4 to identify samples with comparable mean host halo masses but with a different radial distribution of satellite galaxies, which is a proxy for the formation time of the haloes. We measure the weak lensing signal for groups with a steeper than average and with a shallower than average satellite distribution and find no sign of halo assembly bias, with the bias ratio of 0.85^{+0.37}_{-0.25}, which is consistent with the Λ cold dark matter prediction. Our galaxy groups have typical masses of 1013 M⊙ h-1, naturally complementing previous studies of halo assembly bias on galaxy cluster scales.

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

Energy Technology Data Exchange (ETDEWEB)

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

2014-10-11

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

HOT-DUST-POOR QUASARS IN MID-INFRARED AND OPTICALLY SELECTED SAMPLES

International Nuclear Information System (INIS)

Hao Heng; Elvis, Martin; Civano, Francesca; Lawrence, Andy

2011-01-01

We show that the hot-dust-poor (HDP) quasars, originally found in the X-ray-selected XMM-COSMOS type 1 active galactic nucleus (AGN) sample, are just as common in two samples selected at optical/infrared wavelengths: the Richards et al. Spitzer/SDSS sample (8.7% ± 2.2%) and the Palomar-Green-quasar-dominated sample of Elvis et al. (9.5% ± 5.0%). The properties of the HDP quasars in these two samples are consistent with the XMM-COSMOS sample, except that, at the 99% (∼ 2.5σ) significance, a larger proportion of the HDP quasars in the Spitzer/SDSS sample have weak host galaxy contributions, probably due to the selection criteria used. Either the host dust is destroyed (dynamically or by radiation) or is offset from the central black hole due to recoiling. Alternatively, the universality of HDP quasars in samples with different selection methods and the continuous distribution of dust covering factor in type 1 AGNs suggest that the range of spectral energy distributions could be related to the range of tilts in warped fueling disks, as in the model of Lawrence and Elvis, with HDP quasars having relatively small warps.

Respondent-Driven Sampling – Testing Assumptions: Sampling with Replacement

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Barash Vladimir D.

2016-03-01

Full Text Available Classical Respondent-Driven Sampling (RDS estimators are based on a Markov Process model in which sampling occurs with replacement. Given that respondents generally cannot be interviewed more than once, this assumption is counterfactual. We join recent work by Gile and Handcock in exploring the implications of the sampling-with-replacement assumption for bias of RDS estimators. We differ from previous studies in examining a wider range of sampling fractions and in using not only simulations but also formal proofs. One key finding is that RDS estimates are surprisingly stable even in the presence of substantial sampling fractions. Our analyses show that the sampling-with-replacement assumption is a minor contributor to bias for sampling fractions under 40%, and bias is negligible for the 20% or smaller sampling fractions typical of field applications of RDS.

Bias due to differential participation in case-control studies and review of available approaches for adjustment.

Science.gov (United States)

Aigner, Annette; Grittner, Ulrike; Becher, Heiko

2018-01-01

Low response rates in epidemiologic research potentially lead to the recruitment of a non-representative sample of controls in case-control studies. Problems in the unbiased estimation of odds ratios arise when characteristics causing the probability of participation are associated with exposure and outcome. This is a specific setting of selection bias and a realistic hazard in many case-control studies. This paper formally describes the problem and shows its potential extent, reviews existing approaches for bias adjustment applicable under certain conditions, compares and applies them. We focus on two scenarios: a characteristic C causing differential participation of controls is linked to the outcome through its association with risk factor E (scenario I), and C is additionally a genuine risk factor itself (scenario II). We further assume external data sources are available which provide an unbiased estimate of C in the underlying population. Given these scenarios, we (i) review available approaches and their performance in the setting of bias due to differential participation; (ii) describe two existing approaches to correct for the bias in both scenarios in more detail; (iii) present the magnitude of the resulting bias by simulation if the selection of a non-representative sample is ignored; and (iv) demonstrate the approaches' application via data from a case-control study on stroke. The bias of the effect measure for variable E in scenario I and C in scenario II can be large and should therefore be adjusted for in any analysis. It is positively associated with the difference in response rates between groups of the characteristic causing differential participation, and inversely associated with the total response rate in the controls. Adjustment in a standard logistic regression framework is possible in both scenarios if the population distribution of the characteristic causing differential participation is known or can be approximated well.

Getting DNA copy numbers without control samples.

Science.gov (United States)

Ortiz-Estevez, Maria; Aramburu, Ander; Rubio, Angel

2012-08-16

The selection of the reference to scale the data in a copy number analysis has paramount importance to achieve accurate estimates. Usually this reference is generated using control samples included in the study. However, these control samples are not always available and in these cases, an artificial reference must be created. A proper generation of this signal is crucial in terms of both noise and bias.We propose NSA (Normality Search Algorithm), a scaling method that works with and without control samples. It is based on the assumption that genomic regions enriched in SNPs with identical copy numbers in both alleles are likely to be normal. These normal regions are predicted for each sample individually and used to calculate the final reference signal. NSA can be applied to any CN data regardless the microarray technology and preprocessing method. It also finds an optimal weighting of the samples minimizing possible batch effects. Five human datasets (a subset of HapMap samples, Glioblastoma Multiforme (GBM), Ovarian, Prostate and Lung Cancer experiments) have been analyzed. It is shown that using only tumoral samples, NSA is able to remove the bias in the copy number estimation, to reduce the noise and therefore, to increase the ability to detect copy number aberrations (CNAs). These improvements allow NSA to also detect recurrent aberrations more accurately than other state of the art methods. NSA provides a robust and accurate reference for scaling probe signals data to CN values without the need of control samples. It minimizes the problems of bias, noise and batch effects in the estimation of CNs. Therefore, NSA scaling approach helps to better detect recurrent CNAs than current methods. The automatic selection of references makes it useful to perform bulk analysis of many GEO or ArrayExpress experiments without the need of developing a parser to find the normal samples or possible batches within the data. The method is available in the open-source R package

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

DEFF Research Database (Denmark)

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

2005-01-01

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

Reflecting Equity and Diversity. Part I: Guidelines and Procedure for Evaluating Bias in Instructional Materials. Part II: Bias Awareness Training Worksheets. Part III: Bias Awareness and Procedure Training Course.

Science.gov (United States)

Bebermeyer, Jim; Edmond, Mary, Ed.

Reflecting a need to prepare students for working in diverse organizations, this document was developed to increase school officials' awareness of bias in instructional materials and help them select bias-free materials. A number of the examples illustrate situations dealing with diversity in the workplace. The guide is divided into three parts:…

Alcohol cognitive bias modification training for problem drinkers over the web.

Science.gov (United States)

Wiers, Reinout W; Houben, Katrijn; Fadardi, Javad S; van Beek, Paul; Rhemtulla, Mijke; Cox, W Miles

2015-01-01

Following successful outcomes of cognitive bias modification (CBM) programs for alcoholism in clinical and community samples, the present study investigated whether different varieties of CBM (attention control training and approach-bias re-training) could be delivered successfully in a fully automated web-based way and whether these interventions would help self-selected problem drinkers to reduce their drinking. Participants were recruited through online advertising, which resulted in 697 interested participants, of whom 615 were screened in. Of the 314 who initiated training, 136 completed a pretest, four sessions of computerized training and a posttest. Participants were randomly assigned to one of four experimental conditions (attention control or one of three varieties of approach-bias re-training) or a sham-training control condition. The general pattern of findings was that participants in all conditions (including participants in the control-training condition) reduced their drinking. It is suggested that integrating CBM with online cognitive and motivational interventions could improve results. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2016-06-01

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

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

Measuring Agricultural Bias

DEFF Research Database (Denmark)

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

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

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

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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.

The NuSTAR  Extragalactic Surveys: X-Ray Spectroscopic Analysis of the Bright Hard-band Selected Sample

Science.gov (United States)

Zappacosta, L.; Comastri, A.; Civano, F.; Puccetti, S.; Fiore, F.; Aird, J.; Del Moro, A.; Lansbury, G. B.; Lanzuisi, G.; Goulding, A.; Mullaney, J. R.; Stern, D.; Ajello, M.; Alexander, D. M.; Ballantyne, D. R.; Bauer, F. E.; Brandt, W. N.; Chen, C.-T. J.; Farrah, D.; Harrison, F. A.; Gandhi, P.; Lanz, L.; Masini, A.; Marchesi, S.; Ricci, C.; Treister, E.

2018-02-01

We discuss the spectral analysis of a sample of 63 active galactic nuclei (AGN) detected above a limiting flux of S(8{--}24 {keV})=7× {10}-14 {erg} {{{s}}}-1 {{cm}}-2 in the multi-tiered NuSTAR extragalactic survey program. The sources span a redshift range z=0{--}2.1 (median =0.58). The spectral analysis is performed over the broad 0.5–24 keV energy range, combining NuSTAR with Chandra and/or XMM-Newton data and employing empirical and physically motivated models. This constitutes the largest sample of AGN selected at > 10 {keV} to be homogeneously spectrally analyzed at these flux levels. We study the distribution of spectral parameters such as photon index, column density ({N}{{H}}), reflection parameter ({\\boldsymbol{R}}), and 10–40 keV luminosity ({L}{{X}}). Heavily obscured ({log}[{N}{{H}}/{{cm}}-2]≥slant 23) and Compton-thick (CT; {log}[{N}{{H}}/{{cm}}-2]≥slant 24) AGN constitute ∼25% (15–17 sources) and ∼2–3% (1–2 sources) of the sample, respectively. The observed {N}{{H}} distribution agrees fairly well with predictions of cosmic X-ray background population-synthesis models (CXBPSM). We estimate the intrinsic fraction of AGN as a function of {N}{{H}}, accounting for the bias against obscured AGN in a flux-selected sample. The fraction of CT AGN relative to {log}[{N}{{H}}/{{cm}}-2]=20{--}24 AGN is poorly constrained, formally in the range 2–56% (90% upper limit of 66%). We derived a fraction (f abs) of obscured AGN ({log}[{N}{{H}}/{{cm}}-2]=22{--}24) as a function of {L}{{X}} in agreement with CXBPSM and previous zvalues.

Negatively-Biased Credulity and the Cultural Evolution of Beliefs

Science.gov (United States)

Fessler, Daniel M. T.; Pisor, Anne C.; Navarrete, Carlos David

2014-01-01

The functions of cultural beliefs are often opaque to those who hold them. Accordingly, to benefit from cultural evolution’s ability to solve complex adaptive problems, learners must be credulous. However, credulity entails costs, including susceptibility to exploitation, and effort wasted due to false beliefs. One determinant of the optimal level of credulity is the ratio between the costs of two types of errors: erroneous incredulity (failing to believe information that is true) and erroneous credulity (believing information that is false). This ratio can be expected to be asymmetric when information concerns hazards, as the costs of erroneous incredulity will, on average, exceed the costs of erroneous credulity; no equivalent asymmetry characterizes information concerning benefits. Natural selection can therefore be expected to have crafted learners’ minds so as to be more credulous toward information concerning hazards. This negatively-biased credulity extends general negativity bias, the adaptive tendency for negative events to be more salient than positive events. Together, these biases constitute attractors that should shape cultural evolution via the aggregated effects of learners’ differential retention and transmission of information. In two studies in the U.S., we demonstrate the existence of negatively-biased credulity, and show that it is most pronounced in those who believe the world to be dangerous, individuals who may constitute important nodes in cultural transmission networks. We then document the predicted imbalance in cultural content using a sample of urban legends collected from the Internet and a sample of supernatural beliefs obtained from ethnographies of a representative collection of the world’s cultures, showing that beliefs about hazards predominate in both. PMID:24736596

Negatively-biased credulity and the cultural evolution of beliefs.

Directory of Open Access Journals (Sweden)

Daniel M T Fessler

Full Text Available The functions of cultural beliefs are often opaque to those who hold them. Accordingly, to benefit from cultural evolution's ability to solve complex adaptive problems, learners must be credulous. However, credulity entails costs, including susceptibility to exploitation, and effort wasted due to false beliefs. One determinant of the optimal level of credulity is the ratio between the costs of two types of errors: erroneous incredulity (failing to believe information that is true and erroneous credulity (believing information that is false. This ratio can be expected to be asymmetric when information concerns hazards, as the costs of erroneous incredulity will, on average, exceed the costs of erroneous credulity; no equivalent asymmetry characterizes information concerning benefits. Natural selection can therefore be expected to have crafted learners' minds so as to be more credulous toward information concerning hazards. This negatively-biased credulity extends general negativity bias, the adaptive tendency for negative events to be more salient than positive events. Together, these biases constitute attractors that should shape cultural evolution via the aggregated effects of learners' differential retention and transmission of information. In two studies in the U.S., we demonstrate the existence of negatively-biased credulity, and show that it is most pronounced in those who believe the world to be dangerous, individuals who may constitute important nodes in cultural transmission networks. We then document the predicted imbalance in cultural content using a sample of urban legends collected from the Internet and a sample of supernatural beliefs obtained from ethnographies of a representative collection of the world's cultures, showing that beliefs about hazards predominate in both.

Accretion Properties of a Sample of Hard X-Ray (<60 keV) Selected Seyfert 1 Galaxies

Science.gov (United States)

Wang, J.; Mao, Y. F.; Wei, J. Y.

2009-02-01

We examine the accretion properties in a sample of 42 hard (3-60 keV) X-ray selected nearby broad-line active galactic nuclei (AGNs). The energy range in the sample is harder than that usually used in similar previous studies. These AGNs are mainly complied from the RXTE All Sky Survey, and complemented by the released INTEGRAL AGN catalog. The black hole masses, bolometric luminosities of AGN, and Eddington ratios are derived from their optical spectra in terms of the broad Hβ emission line. The tight correlation between the hard X-ray (3-20 keV) and bolometric/line luminosity is well identified in our sample. Also identified is a strong inverse Baldwin relationship of the Hβ emission line. In addition, all of these hard X-ray AGNs are biased toward luminous objects with a high Eddington ratio (mostly between 0.01 and 0.1) and a low column density (Simbol-X, and NeXT. Finally, the correlation between RFe (= optical Fe II/Hβ) and disk temperature as assessed by T vprop (L/L Edd)M -1 BH leads us to suggest that the strength of the Fe II emission is mainly determined by the shape of the ionizing spectrum.

A novel heterogeneous training sample selection method on space-time adaptive processing

Science.gov (United States)

Wang, Qiang; Zhang, Yongshun; Guo, Yiduo

2018-04-01

The performance of ground target detection about space-time adaptive processing (STAP) decreases when non-homogeneity of clutter power is caused because of training samples contaminated by target-like signals. In order to solve this problem, a novel nonhomogeneous training sample selection method based on sample similarity is proposed, which converts the training sample selection into a convex optimization problem. Firstly, the existing deficiencies on the sample selection using generalized inner product (GIP) are analyzed. Secondly, the similarities of different training samples are obtained by calculating mean-hausdorff distance so as to reject the contaminated training samples. Thirdly, cell under test (CUT) and the residual training samples are projected into the orthogonal subspace of the target in the CUT, and mean-hausdorff distances between the projected CUT and training samples are calculated. Fourthly, the distances are sorted in order of value and the training samples which have the bigger value are selective preference to realize the reduced-dimension. Finally, simulation results with Mountain-Top data verify the effectiveness of the proposed method.

Bias aware Kalman filters

DEFF Research Database (Denmark)

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

2006-01-01

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

Spanish exit polls. Sampling error or nonresponse bias?

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Pavía, Jose M.

2016-09-01

Full Text Available Countless examples of misleading forecasts on behalf of both pre-election and exit polls can be found all over the world. Non-representative samples due to differential nonresponse have been claimed as being the main reason for inaccurate exit-poll projections. In real inference problems, it is seldom possible to compare estimates and true values. Electoral forecasts are an exception. Comparisons between estimates and final outcomes can be carried out once votes have been tallied. In this paper, we examine the raw data collected in seven exit polls conducted in Spain and test the likelihood that the data collected in each sampled voting location can be considered as a random sample of actual results. Knowing the answer to this is relevant for both electoral analysts and forecasters as, if the hypothesis is rejected, the shortcomings of the collected data would need amending. Analysts could improve the quality of their computations by implementing local correction strategies. We find strong evidence of nonsampling error in Spanish exit polls and evidence that the political context matters. Nonresponse bias is larger in polarized elections and in a climate of fearExiste un gran número de ejemplos de predicciones inexactas obtenidas a partir tanto de encuestas pre-electorales como de encuestas a pie de urna a lo largo del mundo. La presencia de tasas de no-respuesta diferencial entre distintos tipos de electores ha sido la principal razón esgrimida para justificar las proyecciones erróneas en las encuestas a pie de urna. En problemas de inferencia rara vez es posible comparar estimaciones y valores reales. Las predicciones electorales son una excepción. La comparación entre estimaciones y resultados finales puede realizarse una vez los votos han sido contabilizados. En este trabajo, examinamos los datos brutos recogidos en siete encuestas a pie de urna realizadas en España y testamos la hipótesis de que los datos recolectados en cada punto

On the Borders of Harmful and Helpful Beauty Biases

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Maria Agthe

2016-06-01

Full Text Available Research with European Caucasian samples demonstrates that attractiveness-based biases in social evaluation depend on the constellation of the sex of the evaluator and the sex of the target: Whereas people generally show positive biases toward attractive opposite-sex persons, they show less positive or even negative biases toward attractive same-sex persons. By examining these biases both within and between different ethnicities, the current studies provide new evidence for both the generalizability and the specificity of these attractiveness-based social perception biases. Examining within-ethnicity effects, Study 1 is the first to demonstrate that samples from diverse ethnic backgrounds parallel the finding of European Caucasian samples: The advantageous or adverse effects of attractiveness depend on the gender constellation of the evaluator and the evaluated person. Examining between-ethnicity effects, Study 2 found that these attractiveness-based biases emerge almost exclusively toward targets of the evaluator’s own ethnic background; these biases were reduced or eliminated for cross-ethnicity evaluations and interaction intentions. We discuss these findings in light of evolutionary principles and reflect on potential interactions between culture and evolved cognitive mechanisms.

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

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Wan-Ling Hsu

2017-08-01

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

A new configurational bias scheme for sampling supramolecular structures

Energy Technology Data Exchange (ETDEWEB)

De Gernier, Robin; Mognetti, Bortolo M., E-mail: bmognett@ulb.ac.be [Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, Code Postal 231, Campus Plaine, B-1050 Brussels (Belgium); Curk, Tine [Department of Chemistry, University of Cambridge, Cambridge CB2 1EW (United Kingdom); Dubacheva, Galina V. [Biosurfaces Unit, CIC biomaGUNE, Paseo Miramon 182, 20009 Donostia - San Sebastian (Spain); Richter, Ralf P. [Biosurfaces Unit, CIC biomaGUNE, Paseo Miramon 182, 20009 Donostia - San Sebastian (Spain); Université Grenoble Alpes, DCM, 38000 Grenoble (France); CNRS, DCM, 38000 Grenoble (France); Max Planck Institute for Intelligent Systems, 70569 Stuttgart (Germany)

2014-12-28

We present a new simulation scheme which allows an efficient sampling of reconfigurable supramolecular structures made of polymeric constructs functionalized by reactive binding sites. The algorithm is based on the configurational bias scheme of Siepmann and Frenkel and is powered by the possibility of changing the topology of the supramolecular network by a non-local Monte Carlo algorithm. Such a plan is accomplished by a multi-scale modelling that merges coarse-grained simulations, describing the typical polymer conformations, with experimental results accounting for free energy terms involved in the reactions of the active sites. We test the new algorithm for a system of DNA coated colloids for which we compute the hybridisation free energy cost associated to the binding of tethered single stranded DNAs terminated by short sequences of complementary nucleotides. In order to demonstrate the versatility of our method, we also consider polymers functionalized by receptors that bind a surface decorated by ligands. In particular, we compute the density of states of adsorbed polymers as a function of the number of ligand–receptor complexes formed. Such a quantity can be used to study the conformational properties of adsorbed polymers useful when engineering adsorption with tailored properties. We successfully compare the results with the predictions of a mean field theory. We believe that the proposed method will be a useful tool to investigate supramolecular structures resulting from direct interactions between functionalized polymers for which efficient numerical methodologies of investigation are still lacking.

Sampling procedure in a willow plantation for estimation of moisture content

DEFF Research Database (Denmark)

Nielsen, Henrik Kofoed; Lærke, Poul Erik; Liu, Na

2015-01-01

Heating value and fuel quality of wood is closely connected to moisture content. In this work the variation of moisture content (MC) of short rotation coppice (SRC) willow shoots is described for five clones during one harvesting season. Subsequently an appropriate sampling procedure minimising...... labour costs and sampling uncertainty is proposed, where the MC of a single stem section with the length of 10–50 cm corresponds to the mean shoot moisture content (MSMC) with a bias of maximum 11 g kg−1. This bias can be reduced by selecting the stem section according to the particular clone...

Media bias and advertising: Evidence from a German car magazine

OpenAIRE

Dewenter, Ralf; Heimeshoff, Ulrich

2014-01-01

This paper investigates the existence of a possible media bias by analyzing the impact of auto- mobile manufacturer’s advertisements on automobile reviews in a leading German car maga- zine. By accounting for both endogeneity and sample selection using a two-step procedure, we find a positive impact of advertising volumes on test scores. The main advantage of our study is the measurement of technical characteristics of cars to explain test scores. Due to this kind of measurement, we avoid ser...

Soil sampling strategies: Evaluation of different approaches

Energy Technology Data Exchange (ETDEWEB)

De Zorzi, Paolo [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Metrologia Ambientale, Via di Castel Romano, 100-00128 Roma (Italy)], E-mail: paolo.dezorzi@apat.it; Barbizzi, Sabrina; Belli, Maria [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Metrologia Ambientale, Via di Castel Romano, 100-00128 Roma (Italy); Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia [Agenzia Regionale per la Prevenzione e Protezione dell' Ambiente del Veneto, ARPA Veneto, U.O. Centro Qualita Dati, Via Spalato, 14-36045 Vicenza (Italy)

2008-11-15

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2{sigma}, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies.

Soil sampling strategies: Evaluation of different approaches

International Nuclear Information System (INIS)

De Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia

2008-01-01

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2σ, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies

Soil sampling strategies: evaluation of different approaches.

Science.gov (United States)

de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia

2008-11-01

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2sigma, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies.

6. Label-free selective plane illumination microscopy of tissue samples

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Muteb Alharbi

2017-10-01

Conclusion: Overall this method meets the demands of the current needs for 3D imaging tissue samples in a label-free manner. Label-free Selective Plane Microscopy directly provides excellent information about the structure of the tissue samples. This work has highlighted the superiority of Label-free Selective Plane Microscopy to current approaches to label-free 3D imaging of tissue.

Attentional bias modification based on visual probe task: methodological issues, results and clinical relevance

Directory of Open Access Journals (Sweden)

Fernanda Machado Lopes

2015-12-01

Full Text Available Introduction: Attentional bias, the tendency that a person has to drive or maintain attention to a specific class of stimuli, may play an important role in the etiology and persistence of mental disorders. Attentional bias modification has been studied as a form of additional treatment related to automatic processing. Objectives: This systematic literature review compared and discussed methods, evidence of success and potential clinical applications of studies about attentional bias modification (ABM using a visual probe task. Methods: The Web of Knowledge, PubMed and PsycInfo were searched using the keywords attentional bias modification, attentional bias manipulation and attentional bias training. We selected empirical studies about ABM training using a visual probe task written in English and published between 2002 and 2014. Results: Fifty-seven studies met inclusion criteria. Most (78% succeeded in training attention in the predicted direction, and in 71% results were generalized to other measures correlated with the symptoms. Conclusions: ABM has potential clinical utility, but to standardize methods and maximize applicability, future studies should include clinical samples and be based on findings of studies about its effectiveness.

Are we under-estimating the association between autism symptoms?: The importance of considering simultaneous selection when using samples of individuals who meet diagnostic criteria for an autism spectrum disorder.

Science.gov (United States)

Murray, Aja Louise; McKenzie, Karen; Kuenssberg, Renate; O'Donnell, Michael

2014-11-01

The magnitude of symptom inter-correlations in diagnosed individuals has contributed to the evidence that autism spectrum disorders (ASD) is a fractionable disorder. Such correlations may substantially under-estimate the population correlations among symptoms due to simultaneous selection on the areas of deficit required for diagnosis. Using statistical simulations of this selection mechanism, we provide estimates of the extent of this bias, given different levels of population correlation between symptoms. We then use real data to compare domain inter-correlations in the Autism Spectrum Quotient, in those with ASD versus a combined ASD and non-ASD sample. Results from both studies indicate that samples restricted to individuals with a diagnosis of ASD potentially substantially under-estimate the magnitude of association between features of ASD.

Sex attracts: investigating individual differences in attentional bias to sexual stimuli.

Directory of Open Access Journals (Sweden)

Sabine Kagerer

Full Text Available We investigated the impact of sexual stimuli and the influence of sexual motivation on the performance in a dot-probe task and a line-orientation task in a large sample of males and females. All pictures (neutral, erotic were rated on the dimensions of valence, arousal, disgust, and sexual arousal. Additionally, questionnaires measuring sexual interest/desire/motivation were employed. The ratings of the sexual stimuli point to a successful picture selection because sexual arousal did not differ between the sexes. The stimuli were equally arousing for men and women. Higher scores in the employed questionnaires measuring sexual interest/desire/motivation led to higher sexual arousal ratings of the sex pictures. Attentional bias towards sex pictures was observed in both experimental tasks. The attentional biases measured by the dot-probe and the line-orientation task were moderately intercorrelated suggesting attentional bias as a possible marker for a sex-attention trait. Finally, only the sexual sensation seeking score correlated with the attentional biases of the two tasks. Future research is needed to increase the predictive power of these indirect measures of sexual interest.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

Science.gov (United States)

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

Bias against research on gender bias.

Science.gov (United States)

Cislak, Aleksandra; Formanowicz, Magdalena; Saguy, Tamar

2018-01-01

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

Response rates and selection problems, with emphasis on mental health variables and DNA sampling, in large population-based, cross-sectional and longitudinal studies of adolescents in Norway

Directory of Open Access Journals (Sweden)

Lien Lars

2010-10-01

Full Text Available Abstract Background Selection bias is a threat to the internal validity of epidemiological studies. In light of a growing number of studies which aim to provide DNA, as well as a considerable number of invitees who declined to participate, we discuss response rates, predictors of lost to follow-up and failure to provide DNA, and the presence of possible selection bias, based on five samples of adolescents. Methods We included nearly 7,000 adolescents from two longitudinal studies of 18/19 year olds with two corresponding cross-sectional baseline studies at age 15/16 (10th graders, and one cross-sectional study of 13th graders (18/19 years old. DNA was sampled from the cheek mucosa of 18/19 year olds. Predictors of lost to follow-up and failure to provide DNA were studied by Poisson regression. Selection bias in the follow-up at age 18/19 was estimated through investigation of prevalence ratios (PRs between selected exposures (physical activity, smoking and outcome variables (general health, mental distress, externalizing problems measured at baseline. Results Out of 5,750 who participated at age 15/16, we lost 42% at follow-up at age 18/19. The percentage of participants who gave their consent to DNA provision was as high as the percentage that consented to a linkage of data with other health registers and surveys, approximately 90%. Significant predictors of lost to follow-up and failure to provide DNA samples in the present genetic epidemiological study were: male gender; non-western ethnicity; postal survey compared with school-based; low educational plans; low education and income of father; low perceived family economy; unmarried parents; poor self-reported health; externalized symptoms and smoking, with some differences in subgroups of ethnicity and gender. The association measures (PRs were quite similar among participants and all invitees, with some minor discrepancies in subgroups of non-western boys and girls. Conclusions Lost to

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

Science.gov (United States)

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

2017-09-01

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

Nonlinear vs. linear biasing in Trp-cage folding simulations

Energy Technology Data Exchange (ETDEWEB)

Spiwok, Vojtěch, E-mail: spiwokv@vscht.cz; Oborský, Pavel; Králová, Blanka [Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Technická 3, Prague 6 166 28 (Czech Republic); Pazúriková, Jana [Institute of Computer Science, Masaryk University, Botanická 554/68a, 602 00 Brno (Czech Republic); Křenek, Aleš [Institute of Computer Science, Masaryk University, Botanická 554/68a, 602 00 Brno (Czech Republic); Center CERIT-SC, Masaryk Univerzity, Šumavská 416/15, 602 00 Brno (Czech Republic)

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

Science.gov (United States)

Stohlgren, Thomas J.; Kumar, Sunil; Barnett, David T.; Evangelista, Paul H.

2011-01-01

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km2) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.

Evaluation of sampling strategies to estimate crown biomass

Directory of Open Access Journals (Sweden)

Krishna P Poudel

2015-01-01

Full Text Available Background Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass. Methods Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE, relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12 are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass. Results Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases. Conclusions Use of

Patch-based visual tracking with online representative sample selection

Science.gov (United States)

Ou, Weihua; Yuan, Di; Li, Donghao; Liu, Bin; Xia, Daoxun; Zeng, Wu

2017-05-01

Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.

Sampling for Patient Exit Interviews: Assessment of Methods Using Mathematical Derivation and Computer Simulations.

Science.gov (United States)

Geldsetzer, Pascal; Fink, Günther; Vaikath, Maria; Bärnighausen, Till

2018-02-01

(1) To evaluate the operational efficiency of various sampling methods for patient exit interviews; (2) to discuss under what circumstances each method yields an unbiased sample; and (3) to propose a new, operationally efficient, and unbiased sampling method. Literature review, mathematical derivation, and Monte Carlo simulations. Our simulations show that in patient exit interviews it is most operationally efficient if the interviewer, after completing an interview, selects the next patient exiting the clinical consultation. We demonstrate mathematically that this method yields a biased sample: patients who spend a longer time with the clinician are overrepresented. This bias can be removed by selecting the next patient who enters, rather than exits, the consultation room. We show that this sampling method is operationally more efficient than alternative methods (systematic and simple random sampling) in most primary health care settings. Under the assumption that the order in which patients enter the consultation room is unrelated to the length of time spent with the clinician and the interviewer, selecting the next patient entering the consultation room tends to be the operationally most efficient unbiased sampling method for patient exit interviews. © 2016 The Authors. Health Services Research published by Wiley Periodicals, Inc. on behalf of Health Research and Educational Trust.

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

Science.gov (United States)

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

2018-07-01

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

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

Science.gov (United States)

Trotta, Edoardo

2016-05-17

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

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

Science.gov (United States)

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

2014-04-01

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

Investigation of attentional bias in obsessive compulsive disorder with and without depression in visual search.

Directory of Open Access Journals (Sweden)

Sharon Morein-Zamir

Full Text Available Whether Obsessive Compulsive Disorder (OCD is associated with an increased attentional bias to emotive stimuli remains controversial. Additionally, it is unclear whether comorbid depression modulates abnormal emotional processing in OCD. This study examined attentional bias to OC-relevant scenes using a visual search task. Controls, non-depressed and depressed OCD patients searched for their personally selected positive images amongst their negative distractors, and vice versa. Whilst the OCD groups were slower than healthy individuals in rating the images, there were no group differences in the magnitude of negative bias to concern-related scenes. A second experiment employing a common set of images replicated the results on an additional sample of OCD patients. Although there was a larger bias to negative OC-related images without pre-exposure overall, no group differences in attentional bias were observed. However, OCD patients subsequently rated the images more slowly and more negatively, again suggesting post-attentional processing abnormalities. The results argue against a robust attentional bias in OCD patients, regardless of their depression status and speak to generalized difficulties disengaging from negative valence stimuli. Rather, post-attentional processing abnormalities may account for differences in emotional processing in OCD.

Bias against foreign-born or foreign-trained doctors: experimental evidence.

Science.gov (United States)

Louis, Winnifred R; Lalonde, Richard N; Esses, Victoria M

2010-12-01

Bias against foreign-born or -trained medical students and doctors is not well understood, despite its documented impact on recruitment, integration and retention. This research experimentally examines the interaction of location of medical education and nationality in evaluations of doctors' competence and trustworthiness. A convenience sample of prospective patients evaluated fictitious candidates for a position as a doctor in community practice at a new local health clinic. All applicants were described as having the same personality profile, legal qualifications to practise, a multi-degree education and relevant work experience. The location of medical education (the candidate's home country or the UK) and national background (Australia or Pakistan) of the applicants were independently experimentally manipulated. Consistent with previous research on skills discounting and bias, foreign-born candidates were evaluated less favourably than native-born candidates, despite their comparable education level, work experience and personality. However, overseas medical education obtained in the First World both boosted evaluations (of competence and trustworthiness) and attenuated bias based on nationality. The present findings demonstrate the selective discounting of foreign-born doctors' credentials. The data show an interaction of location of medical education and birth nationality in bias against foreign doctors. On an applied level, the data document that the benefits of medical education obtained in the First World can extend beyond its direct outcomes (high-quality training and institutional recognition) to the indirect benefit of the attenuation of patient bias based on nationality. © Blackwell Publishing Ltd 2010.

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

Science.gov (United States)

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

Variance bias analysis for the Gelbard's batch method

Energy Technology Data Exchange (ETDEWEB)

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

2014-05-15

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

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

Science.gov (United States)

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-05-22

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

Removing Malmquist bias from linear regressions

Science.gov (United States)

Verter, Frances

1993-01-01

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

Biased agonism of the calcium-sensing receptor

DEFF Research Database (Denmark)

Thomsen, Alex Rojas Bie; Hvidtfeldt, Maja; Bräuner-Osborne, Hans

2012-01-01

After the discovery of molecules modulating G protein-coupled receptors (GPCRs) that are able to selectively affect one signaling pathway over others for a specific GPCR, thereby "biasing" the signaling, it has become obvious that the original model of GPCRs existing in either an "on" or "off...... through recruitment of ß-arrestins. Next, by measuring activity of all three signaling pathways we found that barium, spermine, neomycin, and tobramycin act as biased agonist in terms of efficacy and/or potency. Finally, polyamines and aminoglycosides in general were biased in their potencies toward ERK1...

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

Indian Academy of Sciences (India)

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

Data Quality Objectives For Selecting Waste Samples For Bench-Scale Reformer Treatability Studies

International Nuclear Information System (INIS)

Banning, D.L.

2011-01-01

This document describes the data quality objectives to select archived samples located at the 222-S Laboratory for Bench-Scale Reforming testing. The type, quantity, and quality of the data required to select the samples for Fluid Bed Steam Reformer testing are discussed. In order to maximize the efficiency and minimize the time to treat Hanford tank waste in the Waste Treatment and Immobilization Plant, additional treatment processes may be required. One of the potential treatment processes is the fluidized bed steam reformer. A determination of the adequacy of the fluidized bed steam reformer process to treat Hanford tank waste is required. The initial step in determining the adequacy of the fluidized bed steam reformer process is to select archived waste samples from the 222-S Laboratory that will be used in a bench scale tests. Analyses of the selected samples will be required to confirm the samples meet the shipping requirements and for comparison to the bench scale reformer (BSR) test sample selection requirements.

Blocked Randomization with Randomly Selected Block Sizes

Directory of Open Access Journals (Sweden)

Jimmy Efird

2010-12-01

Full Text Available When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.

Getting DNA copy numbers without control samples

Directory of Open Access Journals (Sweden)

Ortiz-Estevez Maria

2012-08-01

Full Text Available Abstract Background The selection of the reference to scale the data in a copy number analysis has paramount importance to achieve accurate estimates. Usually this reference is generated using control samples included in the study. However, these control samples are not always available and in these cases, an artificial reference must be created. A proper generation of this signal is crucial in terms of both noise and bias. We propose NSA (Normality Search Algorithm, a scaling method that works with and without control samples. It is based on the assumption that genomic regions enriched in SNPs with identical copy numbers in both alleles are likely to be normal. These normal regions are predicted for each sample individually and used to calculate the final reference signal. NSA can be applied to any CN data regardless the microarray technology and preprocessing method. It also finds an optimal weighting of the samples minimizing possible batch effects. Results Five human datasets (a subset of HapMap samples, Glioblastoma Multiforme (GBM, Ovarian, Prostate and Lung Cancer experiments have been analyzed. It is shown that using only tumoral samples, NSA is able to remove the bias in the copy number estimation, to reduce the noise and therefore, to increase the ability to detect copy number aberrations (CNAs. These improvements allow NSA to also detect recurrent aberrations more accurately than other state of the art methods. Conclusions NSA provides a robust and accurate reference for scaling probe signals data to CN values without the need of control samples. It minimizes the problems of bias, noise and batch effects in the estimation of CNs. Therefore, NSA scaling approach helps to better detect recurrent CNAs than current methods. The automatic selection of references makes it useful to perform bulk analysis of many GEO or ArrayExpress experiments without the need of developing a parser to find the normal samples or possible batches within the

A "Scientific Diversity" Intervention to Reduce Gender Bias in a Sample of Life Scientists

Science.gov (United States)

Moss-Racusin, Corinne A.; van der Toorn, Jojanneke; Dovidio, John F.; Brescoll, Victoria L.; Graham, Mark J.; Handelsman, Jo

2016-01-01

Mounting experimental evidence suggests that subtle gender biases favoring men contribute to the underrepresentation of women in science, technology, engineering, and mathematics (STEM), including many subfields of the life sciences. However, there are relatively few evaluations of diversity interventions designed to reduce gender biases within…

Can Memory Bias be Modified? The Effects of an Explicit Cued-Recall Training in Two Independent Samples

NARCIS (Netherlands)

Vrijsen, J.N.; Becker, E.S.; Rinck, M.; Oostrom, I.I.H. van; Speckens, A.E.M.; Whitmer, A.; Gotlib, I.H.

2014-01-01

Cognitive bias modification (CBM) has been found to be effective in modifying information-processing biases and in reducing emotional reactivity to stress. Although modification of attention and interpretation biases has frequently been studied, it is not clear whether memory bias can be manipulated

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

Science.gov (United States)

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data.

Science.gov (United States)

Huang, Da-Cang; Wang, Jin-Feng; Huang, Ji-Xia; Sui, Daniel Z; Zhang, Hong-Yan; Hu, Mao-Gui; Xu, Cheng-Dong

2016-06-01

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

Robust online tracking via adaptive samples selection with saliency detection

Science.gov (United States)

Yan, Jia; Chen, Xi; Zhu, QiuPing

2013-12-01

Online tracking has shown to be successful in tracking of previously unknown objects. However, there are two important factors which lead to drift problem of online tracking, the one is how to select the exact labeled samples even when the target locations are inaccurate, and the other is how to handle the confusors which have similar features with the target. In this article, we propose a robust online tracking algorithm with adaptive samples selection based on saliency detection to overcome the drift problem. To deal with the problem of degrading the classifiers using mis-aligned samples, we introduce the saliency detection method to our tracking problem. Saliency maps and the strong classifiers are combined to extract the most correct positive samples. Our approach employs a simple yet saliency detection algorithm based on image spectral residual analysis. Furthermore, instead of using the random patches as the negative samples, we propose a reasonable selection criterion, in which both the saliency confidence and similarity are considered with the benefits that confusors in the surrounding background are incorporated into the classifiers update process before the drift occurs. The tracking task is formulated as a binary classification via online boosting framework. Experiment results in several challenging video sequences demonstrate the accuracy and stability of our tracker.

40 CFR 205.57-2 - Test vehicle sample selection.

Science.gov (United States)

2010-07-01

... pursuant to a test request in accordance with this subpart will be selected in the manner specified in the... then using a table of random numbers to select the number of vehicles as specified in paragraph (c) of... with the desig-nated AQL are contained in Appendix I, -Table II. (c) The appropriate batch sample size...

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

Directory of Open Access Journals (Sweden)

Anis JARBOUI

2012-12-01

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

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

Science.gov (United States)

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

2016-02-01

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

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

Science.gov (United States)

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

2015-01-01

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

Does duration of untreated psychosis bias study samples of first-episode psychosis?

DEFF Research Database (Denmark)

Friis, S; Melle, I; Larsen, T K

2004-01-01

OBJECTIVE: While findings are contradictory, many studies report that long Duration of Untreated Psychosis (DUP) correlates with poorer outcome in first episode psychosis. In an outcome study of first-episode psychosis, we compared the patients who refused to participate in a follow-along with th......OBJECTIVE: While findings are contradictory, many studies report that long Duration of Untreated Psychosis (DUP) correlates with poorer outcome in first episode psychosis. In an outcome study of first-episode psychosis, we compared the patients who refused to participate in a follow......-along with those who consented to estimate the importance of this factor in sample recruitment bias. Our questions were: (i) What is the percentage of refusers? (ii) Are there systematic differences between refusers and consenters on DUP and/or other admission variables? (iii) What is the risk of refusal...... for different values of DUP? METHOD: In an unselected group of consecutively admitted patients we compared follow-along refusers and consenters on the following admission variables: sex, age, diagnostic group, substance abuse, being in-patient, coming from an early detection site and DUP. We conducted...

Procedures for Dealing with Optimism Bias in Transport Planning

DEFF Research Database (Denmark)

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

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

Selection of the Sample for Data-Driven $Z \\to \

CERN Document Server

Krauss, Martin

2009-01-01

The topic of this study was to improve the selection of the sample for data-driven Z → ν ν background estimation, which is a major contribution in supersymmetric searches in ̄ a no-lepton search mode. The data is based on Z → + − samples using data created with ATLAS simulation software. This method works if two leptons are reconstructed, but using cuts that are typical for SUSY searches reconstruction efficiency for electrons and muons is rather low. For this reason it was tried to enhance the data sample. Therefore events were considered, where only one electron was reconstructed. In this case the invariant mass for the electron and each jet was computed to select the jet with the best match for the Z boson mass as not reconstructed electron. This way the sample can be extended but significantly looses purity because of also reconstructed background events. To improve this method other variables have to be considered which were not available for this study. Applying a similar method to muons using ...

Detailed RIF decomposition with selection : the gender pay gap in Italy

OpenAIRE

Töpfer, Marina

2017-01-01

In this paper, we estimate the gender pay gap along the wage distribution using a detailed decomposition approach based on unconditional quantile regressions. Non-randomness of the sample leads to biased and inconsistent estimates of the wage equation as well as of the components of the wage gap. Therefore, the method is extended to account for sample selection problems. The decomposition is conducted by using Italian microdata. Accounting for labor market selection may be particularly rele...

Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

Directory of Open Access Journals (Sweden)

Martinásková Magdalena

2017-12-01

Full Text Available The article examines the use of Asymptotic Sampling (AS for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions have been selected in order to test AS with various sampling schemes: (i Monte Carlo designs; (ii LHS designs optimized using the Periodic Audze-Eglājs (PAE criterion; (iii designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.

Eating disorder symptoms and autobiographical memory bias in an analogue sample

NARCIS (Netherlands)

Wessel, Ineke; Huntjens, Rafaële

2016-01-01

Cognitive theories hold that dysfunctional cognitive schemas and associated information-processing biases are involved in the maintenance of psychopathology. In eating disorders (ED), these schemas would consist of self-evaluative representations, in which the importance of controlling eating, shape

On the relative independence of thinking biases and cognitive ability.

Science.gov (United States)

Stanovich, Keith E; West, Richard F

2008-04-01

In 7 different studies, the authors observed that a large number of thinking biases are uncorrelated with cognitive ability. These thinking biases include some of the most classic and well-studied biases in the heuristics and biases literature, including the conjunction effect, framing effects, anchoring effects, outcome bias, base-rate neglect, "less is more" effects, affect biases, omission bias, myside bias, sunk-cost effect, and certainty effects that violate the axioms of expected utility theory. In a further experiment, the authors nonetheless showed that cognitive ability does correlate with the tendency to avoid some rational thinking biases, specifically the tendency to display denominator neglect, probability matching rather than maximizing, belief bias, and matching bias on the 4-card selection task. The authors present a framework for predicting when cognitive ability will and will not correlate with a rational thinking tendency. (c) 2008 APA, all rights reserved.

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

OpenAIRE

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-01-01

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

Biases in Visual, Auditory, and Audiovisual Perception of Space

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Brian Odegaard

2015-12-01

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

Bounding the bias of contrastive divergence learning

DEFF Research Database (Denmark)

Fischer, Anja; Igel, Christian

2011-01-01

Optimization based on k-step contrastive divergence (CD) has become a common way to train restricted Boltzmann machines (RBMs). The k-step CD is a biased estimator of the log-likelihood gradient relying on Gibbs sampling. We derive a new upper bound for this bias. Its magnitude depends on k...

Effects of social organization, trap arrangement and density, sampling scale, and population density on bias in population size estimation using some common mark-recapture estimators.

Directory of Open Access Journals (Sweden)

Manan Gupta

Full Text Available Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates

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

African Journals Online (AJOL)

sunny t

2016-02-24

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

Galaxy bias from galaxy-galaxy lensing in the DES Science Verification Data

Energy Technology Data Exchange (ETDEWEB)

Prat, J.; et al.

2016-09-26

We present a measurement of galaxy-galaxy lensing around a magnitude-limited ($i_{AB} < 22.5$) sample of galaxies selected from the Dark Energy Survey Science Verification (DES-SV) data. We split these lenses into three photometric-redshift bins from 0.2 to 0.8, and determine the product of the galaxy bias $b$ and cross-correlation coefficient between the galaxy and dark matter overdensity fields $r$ in each bin, using scales above 4 Mpc/$h$ comoving, where we find the linear bias model to be valid given our current uncertainties. We compare our galaxy bias results from galaxy-galaxy lensing with those obtained from galaxy clustering (Crocce et al. 2016) and CMB lensing (Giannantonio et al. 2016) for the same sample of galaxies, and find our measurements to be in good agreement with those in Crocce et al. (2016), while, in the lowest redshift bin ($z\\sim0.3$), they show some tension with the findings in Giannantonio et al. (2016). Our results are found to be rather insensitive to a large range of systematic effects. We measure $b\\cdot r$ to be $0.87\\pm 0.11$, $1.12 \\pm 0.16$ and $1.24\\pm 0.23$, respectively for the three redshift bins of width $\\Delta z = 0.2$ in the range $0.2sample, TPZ, leads to changes in the measured biases at the 10-20% level, but it does not alter the main conclusion of this work: when comparing with Crocce et al. (2016) we do not find strong evidence for a cross-correlation parameter significantly below one in this galaxy sample, except possibly at the lowest redshift bin ($z\\sim 0.3$), where we find $r = 0.71 \\pm 0.11$ when using TPZ, and $0.83 \\pm 0.12$ with BPZ, assuming the difference between the results from the two probes can be solely attributed to the cross-correlation parameter.

Bayesian Model Selection under Time Constraints

Science.gov (United States)

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

2017-12-01

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

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

Science.gov (United States)

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

2012-06-01

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

On the Borders of Harmful and Helpful Beauty Biases

OpenAIRE

Maria Agthe; Maria Strobel; Matthias Spörrle; Michaela Pfundmair; Jon K. Maner

2016-01-01

Research with European Caucasian samples demonstrates that attractiveness-based biases in social evaluation depend on the constellation of the sex of the evaluator and the sex of the target: Whereas people generally show positive biases toward attractive opposite-sex persons, they show less positive or even negative biases toward attractive same-sex persons. By examining these biases both within and between different ethnicities, the current studies provide new evidence for both the generaliz...

Recognizing and reducing cognitive bias in clinical and forensic neurology.

Science.gov (United States)

Satya-Murti, Saty; Lockhart, Joseph

2015-10-01

In medicine, cognitive errors form the basis of bias in clinical practice. Several types of bias are common and pervasive, and may lead to inaccurate diagnosis or treatment. Forensic and clinical neurology, even when aided by current technologies, are still dependent on cognitive interpretations, and therefore prone to bias. This article discusses 4 common biases that can lead the clinician astray. They are confirmation bias (selective gathering of and neglect of contradictory evidence); base rate bias (ignoring or misusing prevailing base rate data); hindsight bias (oversimplification of past causation); and good old days bias (the tendency for patients to misremember and exaggerate their preinjury functioning). We briefly describe strategies adopted from the field of psychology that could minimize bias. While debiasing is not easy, reducing such errors requires awareness and acknowledgment of our susceptibility to these cognitive distortions.

A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator

Directory of Open Access Journals (Sweden)

Munir Ahmed

2016-06-01

Full Text Available In the presence of heteroscedasticity, different available flavours of the heteroscedasticity consistent covariance estimator (HCCME are used. However, the available literature shows that these estimators can be considerably biased in small samples. Cribari–Neto et al. (2000 introduce a bias adjustment mechanism and give the modified White estimator that becomes almost bias-free even in small samples. Extending these results, Cribari-Neto and Galvão (2003 present a similar bias adjustment mechanism that can be applied to a wide class of HCCMEs’. In the present article, we follow the same mechanism as proposed by Cribari-Neto and Galvão to give bias-correction version of HCCME but we use adaptive HCCME rather than the conventional HCCME. The Monte Carlo study is used to evaluate the performance of our proposed estimators.

40 CFR 205.171-2 - Test exhaust system sample selection and preparation.

Science.gov (United States)

2010-07-01

... Systems § 205.171-2 Test exhaust system sample selection and preparation. (a)(1) Exhaust systems... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Test exhaust system sample selection and preparation. 205.171-2 Section 205.171-2 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY...

A New Source Biasing Approach in ADVANTG

International Nuclear Information System (INIS)

Bevill, Aaron M.; Mosher, Scott W.

2012-01-01

The ADVANTG code has been developed at Oak Ridge National Laboratory to generate biased sources and weight window maps for MCNP using the CADIS and FW-CADIS methods. In preparation for an upcoming RSICC release, a new approach for generating a biased source has been developed. This improvement streamlines user input and improves reliability. Previous versions of ADVANTG generated the biased source from ADVANTG input, writing an entirely new general fixed-source definition (SDEF). Because volumetric sources were translated into SDEF-format as a finite set of points, the user had to perform a convergence study to determine whether the number of source points used accurately represented the source region. Further, the large number of points that must be written in SDEF-format made the MCNP input and output files excessively long and difficult to debug. ADVANTG now reads SDEF-format distributions and generates corresponding source biasing cards, eliminating the need for a convergence study. Many problems of interest use complicated source regions that are defined using cell rejection. In cell rejection, the source distribution in space is defined using an arbitrarily complex cell and a simple bounding region. Source positions are sampled within the bounding region but accepted only if they fall within the cell; otherwise, the position is resampled entirely. When biasing in space is applied to sources that use rejection sampling, current versions of MCNP do not account for the rejection in setting the source weight of histories, resulting in an 'unfair game'. This problem was circumvented in previous versions of ADVANTG by translating volumetric sources into a finite set of points, which does not alter the mean history weight ((bar w)). To use biasing parameters without otherwise modifying the original cell-rejection SDEF-format source, ADVANTG users now apply a correction factor for (bar w) in post-processing. A stratified-random sampling approach in ADVANTG is under

An improved selective sampling method

International Nuclear Information System (INIS)

Miyahara, Hiroshi; Iida, Nobuyuki; Watanabe, Tamaki

1986-01-01

The coincidence methods which are currently used for the accurate activity standardisation of radio-nuclides, require dead time and resolving time corrections which tend to become increasingly uncertain as countrates exceed about 10 K. To reduce the dependence on such corrections, Muller, in 1981, proposed the selective sampling method using a fast multichannel analyser (50 ns ch -1 ) for measuring the countrates. It is, in many ways, more convenient and possibly potentially more reliable to replace the MCA with scalers and a circuit is described employing five scalers; two of them serving to measure the background correction. Results of comparisons using our new method and the coincidence method for measuring the activity of 60 Co sources yielded agree-ment within statistical uncertainties. (author)

Monolithic Integration of Sampled Grating DBR with Electroabsorption Modulator by Combining Selective-Area-Growth MOCVD and Quantum-Well Intermixing

International Nuclear Information System (INIS)

Hong-Bo, Liu; Ling-Juan, Zhao; Jiao-Qing, Pan; Hong-Liang, Zhu; Fan, Zhou; Bao-Jun, Wang; Wei, Wang

2008-01-01

We present the monolithic integration of a sampled-grating distributed Bragg reflector (SG-DBR) laser with a quantum-well electroabsorption modulator (QW-EAM) by combining ultra-low-pressure (55mbar) selective-area-growth (SAG) metal-organic chemical vapour deposition (MOCVD) and quantum-well intermixing (QWI) for the first time. The QW-EAM and the gain section can be grown simultaneously by using SAG MOCVD technology. Meanwhile, the QWI technology offers an abrupt band-gap change between two functional sections, which reduces internal absorption loss. The experimental results show that the threshold current Ith = 62 mA, and output power reaches 3.6mW. The wavelength tuning range covers 30nm, and all the corresponding side mode suppression ratios are over 30 dB. The extinction ratios at available wavelength channels can reach more than 14 dB with bias of -5 V

New Trends in Magnetic Exchange Bias

Science.gov (United States)

Mougin, Alexandra; Mangin, Stéphane; Bobo, Jean-Francois; Loidl, Alois

2005-05-01

of the constituant layers. The spirit of this topical issue is, for the first time, to gather and survey recent and original developments, both experimental and theoretical, which bring new insights into the physics of exchange bias. It has been planned in relation with an international workshop exclusively devoted to exchange bias, namely IWEBMN’04 (International Workshop on Exchange Bias in Magnetic Nanostructures) that took place in Anglet, in the south west of France, from 16th to 18th September 2004. The conference gathered worldwide researchers in the area, both experimentalists and theoreticians. Several research paths are particularly active in the field of magnetic exchange coupling. The conference, as well as this topical issue, which was also open to contributions from scientists not participating in the conference, has been organized according to the following principles: 1. Epitaxial systems: Since the essential behavior of exchange bias critically depends on the atomic-level chemical and spin structure at the interface between the ferromagnetic and antiferromagnetic components, epitaxial AF/F systems in which the quality of the interface and the crystalline coherence are optimized and well known are ideal candidates for a better understanding of the underlying physics of exchange bias. The dependence of exchange bias on the spin configurations at the interfaces can be accomplished by selecting different crystallographic orientations. The role of interface roughness can also be understood from thin-film systems by changing the growth parameters, and correlations between the interface structure and exchange bias can be made, as reported in this issue. 2. Out-of-plane magnetized systems: While much important work has been devoted to the study of structures with in-plane magnetization, little has been done on the study of exchange bias and exchange coupling in samples with out-of-plane magnetization. Some systems can exhibit either in-plane or out

Measuring agricultural policy bias

DEFF Research Database (Denmark)

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

2010-01-01

Measurement is a key issue in the literature on price incentive bias induced by trade policy. We introduce a general equilibrium measure of the relative effective rate of protection, which generalizes earlier protection measures. For our fifteen sample countries, results indicate that the agricul...

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

Science.gov (United States)

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

2014-01-01

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

Reducing status quo bias in choice experiments

DEFF Research Database (Denmark)

Bonnichsen, Ole; Ladenburg, Jacob

In stated preference literature, the tendency to choose the alternative representing the status quo situation seems to exceed real life status quo effects. Accordingly, status quo bias can be a problem. In Choice Experiments, status quo bias is found to be strongly correlated with protest attitudes...... toward the cost attribute. If economic values are to be elicited, this problem is difficult to remedy. In a split sample framework we test a novel ex-ante entreaty aimed specifically at the cost attribute and find that it effectively reduces status quo bias and improves the internal validity...

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.

The construct equivalence and item bias of the pib/SpEEx conceptualisation-ability test for members of five language groups in South Africa

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Pieter Schaap

2008-11-01

Full Text Available This study’s objective was to determine whether the Potential Index Batteries/Situation Specific Evaluation Expert (PIB/SpEEx conceptualisation (100 ability test displays construct equivalence and item bias for members of five selected language groups in South Africa. The sample consisted of a non-probability convenience sample (N = 6 261 of members of five language groups (speakers of Afrikaans, English, North Sotho, Setswana and isiZulu working in the medical and beverage industries or studying at higher-educational institutions. Exploratory factor analysis with target rotations confrmed the PIB/SpEEx 100’s construct equivalence for the respondents from these five language groups. No evidence of either uniform or non-uniform item bias of practical signifcance was found for the sample.

Mineral Composition of Selected Serbian Propolis Samples

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Tosic Snezana

2017-06-01

Full Text Available The aim of this work was to determine the content of 22 macro- and microelements in ten raw Serbian propolis samples which differ in geographical and botanical origin as well as in polluted agent contents by atomic emission spectrometry with inductively coupled plasma (ICP-OES. The macroelements were more common and present Ca content was the highest while Na content the lowest. Among the studied essential trace elements Fe was the most common element. The levels of toxic elements (Pb, Cd, As and Hg were also analyzed, since they were possible environmental contaminants that could be transferred into propolis products for human consumption. As and Hg were not detected in any of the analyzed samples but a high level of Pb (2.0-9.7 mg/kg was detected and only selected portions of raw propolis could be used to produce natural medicines and dietary supplements for humans. Obtained results were statistically analyzed, and the examined samples showed a wide range of element content.

Effect of Malmquist bias on correlation studies with IRAS data base

Science.gov (United States)

Verter, Frances

1993-01-01

The relationships between galaxy properties in the sample of Trinchieri et al. (1989) are reexamined with corrections for Malmquist bias. The linear correlations are tested and linear regressions are fit for log-log plots of L(FIR), L(H-alpha), and L(B) as well as ratios of these quantities. The linear correlations for Malmquist bias are corrected using the method of Verter (1988), in which each galaxy observation is weighted by the inverse of its sampling volume. The linear regressions are corrected for Malmquist bias by a new method invented here in which each galaxy observation is weighted by its sampling volume. The results of correlation and regressions among the sample are significantly changed in the anticipated sense that the corrected correlation confidences are lower and the corrected slopes of the linear regressions are lower. The elimination of Malmquist bias eliminates the nonlinear rise in luminosity that has caused some authors to hypothesize additional components of FIR emission.

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

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

2017-12-01

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

Randomized clinical trials in dentistry: Risks of bias, risks of random errors, reporting quality, and methodologic quality over the years 1955-2013.

Directory of Open Access Journals (Sweden)

Humam Saltaji

Full Text Available To examine the risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions and the development of these aspects over time.We included 540 randomized clinical trials from 64 selected systematic reviews. We extracted, in duplicate, details from each of the selected randomized clinical trials with respect to publication and trial characteristics, reporting and methodologic characteristics, and Cochrane risk of bias domains. We analyzed data using logistic regression and Chi-square statistics.Sequence generation was assessed to be inadequate (at unclear or high risk of bias in 68% (n = 367 of the trials, while allocation concealment was inadequate in the majority of trials (n = 464; 85.9%. Blinding of participants and blinding of the outcome assessment were judged to be inadequate in 28.5% (n = 154 and 40.5% (n = 219 of the trials, respectively. A sample size calculation before the initiation of the study was not performed/reported in 79.1% (n = 427 of the trials, while the sample size was assessed as adequate in only 17.6% (n = 95 of the trials. Two thirds of the trials were not described as double blinded (n = 358; 66.3%, while the method of blinding was appropriate in 53% (n = 286 of the trials. We identified a significant decrease over time (1955-2013 in the proportion of trials assessed as having inadequately addressed methodological quality items (P < 0.05 in 30 out of the 40 quality criteria, or as being inadequate (at high or unclear risk of bias in five domains of the Cochrane risk of bias tool: sequence generation, allocation concealment, incomplete outcome data, other sources of bias, and overall risk of bias.The risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions have improved over time; however, further efforts that contribute to the development of more stringent

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

Science.gov (United States)

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

2004-01-01

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

Attentional bias in math anxiety.

Science.gov (United States)

Rubinsten, Orly; Eidlin, Hili; Wohl, Hadas; Akibli, Orly

2015-01-01

Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety (MA) as well (i.e., a persistent negative reaction to math). Twenty seven participants (14 with high levels of MA and 13 with low levels of MA) were presented with a novel computerized numerical version of the well established dot probe task. One of six types of prime stimuli, either math related or typically neutral, was presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks) that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks). Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in MA. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words). These findings suggest that attentional bias is linked to unduly intense MA symptoms.

Is there gender bias in nursing research?

Science.gov (United States)

Polit, Denise F; Beck, Cheryl Tatano

2008-10-01

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

Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

NARCIS (Netherlands)

G. Baquero; J.R. ter Horst (Jenke); M.J.C.M. Verbeek (Marno)

2005-01-01

textabstractWe analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in

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

Science.gov (United States)

Jacobs, P; Baladi, J F

1996-01-01

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

Evidence of sex-bias in gene expression in the brain transcriptome of two populations of rainbow trout (Oncorhynchus mykiss) with divergent life histories.

Science.gov (United States)

Hale, Matthew C; McKinney, Garrett J; Thrower, Frank P; Nichols, Krista M

2018-01-01

Sex-bias in gene expression is a mechanism that can generate phenotypic variance between the sexes, however, relatively little is known about how patterns of sex-bias vary during development, and how variable sex-bias is between different populations. To that end, we measured sex-bias in gene expression in the brain transcriptome of rainbow trout (Oncorhynchus mykiss) during the first two years of development. Our sampling included from the fry stage through to when O. mykiss either migrate to the ocean or remain resident and undergo sexual maturation. Samples came from two F1 lines: One from migratory steelhead trout and one from resident rainbow trout. All samples were reared in a common garden environment and RNA sequencing (RNA-seq) was used to estimate patterns of gene expression. A total of 1,716 (4.6% of total) genes showed evidence of sex-bias in gene expression in at least one time point. The majority (96.7%) of sex-biased genes were differentially expressed during the second year of development, indicating that patterns of sex-bias in expression are tied to key developmental events, such as migration and sexual maturation. Mapping of differentially expressed genes to the O. mykiss genome revealed that the X chromosome is enriched for female upregulated genes, and this may indicate a lack of dosage compensation in rainbow trout. There were many more sex-biased genes in the migratory line than the resident line suggesting differences in patterns of gene expression in the brain between populations subjected to different forces of selection. Overall, our results suggest that there is considerable variation in the extent and identity of genes exhibiting sex-bias during the first two years of life. These differentially expressed genes may be connected to developmental differences between the sexes, and/or between adopting a resident or migratory life history.

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.

The self-attribution bias and paranormal beliefs.

Science.gov (United States)

van Elk, Michiel

2017-03-01

The present study investigated the relation between paranormal beliefs, illusory control and the self-attribution bias, i.e., the motivated tendency to attribute positive outcomes to oneself while negative outcomes are externalized. Visitors of a psychic fair played a card guessing game and indicated their perceived control over randomly selected cards as a function of the congruency and valence of the card. A stronger self-attribution bias was observed for paranormal believers compared to skeptics and this bias was specifically related to traditional religious beliefs and belief in superstition. No relation between paranormal beliefs and illusory control was found. Self-report measures indicated that paranormal beliefs were associated to being raised in a spiritual family and to anomalous experiences during childhood. Thereby this study suggests that paranormal beliefs are related to specific cognitive biases that in turn are shaped by socio-cultural factors. Copyright © 2017 Elsevier Inc. All rights reserved.

Cognitive interference and a food-related memory bias in binge eating disorder.

Science.gov (United States)

Svaldi, Jennifer; Schmitz, Florian; Trentowska, Monika; Tuschen-Caffier, Brunna; Berking, Matthias; Naumann, Eva

2014-01-01

The present study was concerned with cognitive interference and a specific memory bias for eating-related stimuli in binge eating disorder (BED). Further objectives were to find out under which circumstances such effects would occur, and whether they are related with each other and with reported severity of BED symptoms. A group of women diagnosed with BED and a matched sample of overweight controls completed two paradigms, an n-back task with lures and a recent-probes task. The BED group generally experienced more interference in the n-back task. Additionally, they revealed selectively increased interference for food items in the recent-probes task. Findings can be reconciled with the view that control functions are generally impaired in BED, and that there is an additional bias for eating-related stimuli, both of which were related with reported severity of BED symptoms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimation and correction of visibility bias in aerial surveys of wintering ducks

Science.gov (United States)

Pearse, A.T.; Gerard, P.D.; Dinsmore, S.J.; Kaminski, R.M.; Reinecke, K.J.

2008-01-01

Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1?100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36?42%, and associated standard errors increased 38?55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species.

Magnification bias corrections to galaxy-lensing cross-correlations

International Nuclear Information System (INIS)

Ziour, Riad; Hui, Lam

2008-01-01

Galaxy-galaxy or galaxy-quasar lensing can provide important information on the mass distribution in the Universe. It consists of correlating the lensing signal (either shear or magnification) of a background galaxy/quasar sample with the number density of a foreground galaxy sample. However, the foreground galaxy density is inevitably altered by the magnification bias due to the mass between the foreground and the observer, leading to a correction to the observed galaxy-lensing signal. The aim of this paper is to quantify this correction. The single most important determining factor is the foreground redshift z f : the correction is small if the foreground galaxies are at low redshifts but can become non-negligible for sufficiently high redshifts. For instance, we find that for the multipole l=1000, the correction is above 1%x(5s f -2)/b f for z f > or approx. 0.37, and above 5%x(5s f -2)/b f for z f > or approx. 0.67, where s f is the number count slope of the foreground sample and b f its galaxy bias. These considerations are particularly important for geometrical measures, such as the Jain and Taylor ratio or its generalization by Zhang et al. Assuming (5s f -2)/b f =1, we find that the foreground redshift should be limited to z f < or approx. 0.45 in order to avoid biasing the inferred dark energy equation of state w by more than 5%, and that even for a low foreground redshift (<0.45), the background samples must be well separated from the foreground to avoid incurring a bias of similar magnitude. Lastly, we briefly comment on the possibility of obtaining these geometrical measures without using galaxy shapes, using instead magnification bias itself.

40 CFR 761.247 - Sample site selection for pipe segment removal.

Science.gov (United States)

2010-07-01

... end of the pipe segment. (3) If the pipe segment is cut with a saw or other mechanical device, take..., take samples from a total of seven segments. (A) Sample the first and last segments removed. (B) Select... total length for purposes of disposal, take samples of each segment that is 1/2 mile distant from the...

A review of cognitive biases in youth depression: attention, interpretation and memory.

Science.gov (United States)

Platt, Belinda; Waters, Allison M; Schulte-Koerne, Gerd; Engelmann, Lina; Salemink, Elske

2017-04-01

Depression is one of the most common mental health problems in childhood and adolescence. Although data consistently show it is associated with self-reported negative cognitive styles, less is known about the mechanisms underlying this relationship. Cognitive biases in attention, interpretation and memory represent plausible mechanisms and are known to characterise adult depression. We provide the first structured review of studies investigating the nature and causal role of cognitive biases in youth depression. Key questions are (i) do cognitive biases characterise youth depression? (ii) are cognitive biases a vulnerability factor for youth depression? and (iii) do cognitive biases play a causal role in youth depression? We find consistent evidence for positive associations between attention and interpretation biases and youth depression. Stronger biases in youth with an elevated risk of depression support cognitive-vulnerability models. Preliminary evidence from cognitive bias modification paradigms supports a causal role of attention and interpretation biases in youth depression but these paradigms require testing in clinical samples before they can be considered treatment tools. Studies of memory biases in youth samples have produced mixed findings and none have investigated the causal role of memory bias. We identify numerous areas for future research in this emerging field.

The proportionator: unbiased stereological estimation using biased automatic image analysis and non-uniform probability proportional to size sampling

DEFF Research Database (Denmark)

Gardi, Jonathan Eyal; Nyengaard, Jens Randel; Gundersen, Hans Jørgen Gottlieb

2008-01-01

examined, which in turn leads to any of the known stereological estimates, including size distributions and spatial distributions. The unbiasedness is not a function of the assumed relation between the weight and the structure, which is in practice always a biased relation from a stereological (integral......, the desired number of fields are sampled automatically with probability proportional to the weight and presented to the expert observer. Using any known stereological probe and estimator, the correct count in these fields leads to a simple, unbiased estimate of the total amount of structure in the sections...... geometric) point of view. The efficiency of the proportionator depends, however, directly on this relation to be positive. The sampling and estimation procedure is simulated in sections with characteristics and various kinds of noises in possibly realistic ranges. In all cases examined, the proportionator...

Bias in estimating food consumption of fish from stomach-content analysis

DEFF Research Database (Denmark)

Rindorf, Anna; Lewy, Peter

2004-01-01

This study presents an analysis of the bias introduced by using simplified methods to calculate food intake of fish from stomach contents. Three sources of bias were considered: (1) the effect of estimating consumption based on a limited number of stomach samples, (2) the effect of using average......, a serious positive bias was introduced by estimating food intake from the contents of pooled stomach samples. An expression is given that can be used to correct analytically for this bias. A new method, which takes into account the distribution and evacuation of individual prey types as well as the effect...... of other food in the stomach on evacuation, is suggested for estimating the intake of separate prey types. Simplifying the estimation by ignoring these factors biased estimates of consumption of individual prey types by up to 150% in a data example....

Reporting bias in medical research - a narrative review

Directory of Open Access Journals (Sweden)

Kölsch Heike

2010-04-01

Full Text Available Abstract Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles. We identified reporting bias in 40 indications comprising around 50 different pharmacological, surgical (e.g. vacuum-assisted closure therapy, diagnostic (e.g. ultrasound, and preventive (e.g. cancer vaccines interventions. Regarding pharmacological interventions, cases of reporting bias were, for example, identified in the treatment of the following conditions: depression, bipolar disorder, schizophrenia, anxiety disorder, attention-deficit hyperactivity disorder, Alzheimer's disease, pain, migraine, cardiovascular disease, gastric ulcers, irritable bowel syndrome, urinary incontinence, atopic dermatitis, diabetes mellitus type 2, hypercholesterolaemia, thyroid disorders, menopausal symptoms, various types of cancer (e.g. ovarian cancer and melanoma, various types of infections (e.g. HIV, influenza and Hepatitis B, and acute trauma. Many cases involved the withholding of study data by manufacturers and regulatory agencies or the active attempt by manufacturers to suppress publication. The ascertained effects of reporting bias included the

A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy

Science.gov (United States)

Kendall, William L.; White, Gary C.

2009-01-01

1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.

Sex bias in classifying borderline and narcissistic personality disorder

NARCIS (Netherlands)

Braamhorst, W.; Lobbestael, J.; Emons, W.H.M.; Arntz, A.; Witteman, C.L.M.; Bekker, M.H.J.

2015-01-01

This study investigated sex bias in the classification of borderline and narcissistic personality disorders. A sample of psychologists in training for a post-master degree (N = 180) read brief case histories (male or female version) and made DSM classification. To differentiate sex bias due to sex

Potential Reporting Bias in Neuroimaging Studies of Sex Differences.

Science.gov (United States)

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

2018-04-17

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

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.

Young children seek out biased information about social groups.

Science.gov (United States)

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

2018-05-01

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

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

International Nuclear Information System (INIS)

Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim

2014-01-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

Energy Technology Data Exchange (ETDEWEB)

Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)

2014-02-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

KAUST Repository

Elsheikh, Ahmed H.

2014-02-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems. © 2013 Elsevier Inc.

Codon usage bias in phylum Actinobacteria: relevance to environmental adaptation and host pathogenicity.

Science.gov (United States)

Lal, Devi; Verma, Mansi; Behura, Susanta K; Lal, Rup

2016-10-01

Actinobacteria are Gram-positive bacteria commonly found in soil, freshwater and marine ecosystems. In this investigation, bias in codon usages of ninety actinobacterial genomes was analyzed by estimating different indices of codon bias such as Nc (effective number of codons), SCUO (synonymous codon usage order), RSCU (relative synonymous codon usage), as well as sequence patterns of codon contexts. The results revealed several characteristic features of codon usage in Actinobacteria, as follows: 1) C- or G-ending codons are used frequently in comparison with A- and U ending codons; 2) there is a direct relationship of GC content with use of specific amino acids such as alanine, proline and glycine; 3) there is an inverse relationship between GC content and Nc estimates, 4) there is low SCUO value (Actinobacteria, extreme GC content and codon bias are driven by mutation rather than natural selection; (2) traits like aerobicity are associated with effective natural selection and therefore low GC content and low codon bias, demonstrating the role of both mutational bias and translational selection in shaping the habitat and phenotype of actinobacterial species. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Attentional Bias in Math Anxiety

Directory of Open Access Journals (Sweden)

Orly eRubinsten

2015-10-01

Full Text Available Cognitive theory from the field of general anxiety suggests that the tendency to display attentional bias toward negative information results in anxiety. Accordingly, the current study aims to investigate whether attentional bias is involved in math anxiety as well (i.e., a persistent negative reaction to math. Twenty seven participants (14 with high levels of math anxiety and 13 with low levels of math anxiety were presented with a novel computerized numerical version of the well established dot probe task. One of 6 types of prime stimuli, either math related or typically neutral, were presented on one side of a computer screen. The prime was preceded by a probe (either one or two asterisks that appeared in either the prime or the opposite location. Participants had to discriminate probe identity (one or two asterisks. Math anxious individuals reacted faster when the probe was at the location of the numerical related stimuli. This suggests the existence of attentional bias in math anxiety. That is, for math anxious individuals, the cognitive system selectively favored the processing of emotionally negative information (i.e., math related words. These findings suggest that attentional bias is linked to unduly intense math anxiety symptoms.

Measurement of radioactivity in the environment - Soil - Part 2: Guidance for the selection of the sampling strategy, sampling and pre-treatment of samples

International Nuclear Information System (INIS)

2007-01-01

This part of ISO 18589 specifies the general requirements, based on ISO 11074 and ISO/IEC 17025, for all steps in the planning (desk study and area reconnaissance) of the sampling and the preparation of samples for testing. It includes the selection of the sampling strategy, the outline of the sampling plan, the presentation of general sampling methods and equipment, as well as the methodology of the pre-treatment of samples adapted to the measurements of the activity of radionuclides in soil. This part of ISO 18589 is addressed to the people responsible for determining the radioactivity present in soil for the purpose of radiation protection. It is applicable to soil from gardens, farmland, urban or industrial sites, as well as soil not affected by human activities. This part of ISO 18589 is applicable to all laboratories regardless of the number of personnel or the range of the testing performed. When a laboratory does not undertake one or more of the activities covered by this part of ISO 18589, such as planning, sampling or testing, the corresponding requirements do not apply. Information is provided on scope, normative references, terms and definitions and symbols, principle, sampling strategy, sampling plan, sampling process, pre-treatment of samples and recorded information. Five annexes inform about selection of the sampling strategy according to the objectives and the radiological characterization of the site and sampling areas, diagram of the evolution of the sample characteristics from the sampling site to the laboratory, example of sampling plan for a site divided in three sampling areas, example of a sampling record for a single/composite sample and example for a sample record for a soil profile with soil description. A bibliography is provided

Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

Energy Technology Data Exchange (ETDEWEB)

Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife

2001-07-01

Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

Quality of reporting and risk of bias in therapeutic otolaryngology publications.

Science.gov (United States)

Kaper, N M; Swart, K M A; Grolman, W; Van Der Heijden, G J M G

2018-01-01

High-quality trials have the potential to influence clinical practice. Ten otolaryngology journals with the highest 2011 impact factors were selected and publications from 2010 were extracted. From all medical journals, the 20 highest impact factor journals were selected, and publications related to otolaryngology for 2010 and 2011 were extracted. For all publications, the reporting quality and risk of bias were assessed. The impact factor was 1.8-2.8 for otolaryngology journals and 6.0-101.8 for medical journals. Of 1500 otolaryngology journal articles, 262 were therapeutic studies; 94 had a high reporting quality and 5 a low risk of bias. Of 10 967 medical journal articles, 76 were therapeutic studies; 57 had a high reporting quality and 8 a low risk of bias. Reporting quality was high for 45 per cent of otolaryngology-related publications and 9 per cent met quality standards. General journals had higher impact factors than otolaryngology journals. Reporting quality was higher and risk of bias lower in general journals than in otolaryngology journals. Nevertheless, 76 per cent of articles in high impact factor journals carried a high risk of bias. Better reported and designed studies are the goal, with less risk of bias, especially in otolaryngology journals.

Hybrid biasing approaches for global variance reduction

International Nuclear Information System (INIS)

Wu, Zeyun; Abdel-Khalik, Hany S.

2013-01-01

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

Effects of habitat features on size-biased predation on salmon by bears.