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Detecting publication selection bias through excess statistical significance
Research Synthesis Methods, 2021We introduce and evaluate three tests for publication selection bias based on excess statistical significance (ESS). The proposed tests incorporate heterogeneity explicitly in the formulas for expected and ESS.
T. Stanley +3 more
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Fair and Robust Classification Under Sample Selection Bias
International Conference on Information and Knowledge Management, 2021To address the sample selection bias between the training and test data, previous research works focus on reweighing biased training data to match the test data and then building classification models on the reweighed training data.
Wei Du, Xintao Wu
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Clarifying selection bias in cluster randomized trials
Clinical Trials, 2021Background In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment.
Fan Li +4 more
semanticscholar +1 more source
Annual Review of Genetics, 2008 In a wide variety of organisms, synonymous codons are used with different frequencies, a phenomenon known as codon bias. Population genetic studies have shown that synonymous sites are under weak selection and that codon bias is maintained by a balance between selection, mutation, and genetic drift.
Dmitri A. Petrov, Ruth Hershberg
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Testing for Selection Bias [PDF]
SSRN Electronic Journal, 2014 This paper uses the control function to develop a framework for testing for selection bias. The idea behind our framework is if the usual assumptions hold for matching or IV estimators, the control function identifies the presence and magnitude of potential selection bias.
Joo, Joonhwi, LaLonde, Robert J.
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Sample selection bias as a specification error
, 1979Sample selection bias as a specification error This paper discusses the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or «omitted variables» bias.
J. Heckman
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One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.
Radiographics, 2020Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or exceeding
Alice C. Yu, J. Eng
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Investigating selection bias of online surveys on coronavirus-related behavioral outcomes
, 2020The coronavirus SARS-CoV-2 outbreak has stimulated numerous online surveys that are mainly based on online convenience samples or commercial online access panels where par-ticipants select themselves.
Ines Schaurer, Bernd Weiss
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Journal of Educational Measurement, 1973 Possible bias in selection procedures used for employment and college admissions is of crucial social and educational importance. However, there are many different definitions of what constitutes bias with each definition based on different values and with different implications for how selection should be accomplished. A number of these definitions of
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Art and gender: market bias or selection bias? [PDF]
Journal of Cultural Economics, 2017 We test for gender effects in the art market using auction prices for artists who graduated from the Yale School of Art. Yale’s female graduates have significantly fewer auction sales, controlling for their graduating year gender ratio. Conditioning upon sale, works by female artists obtained higher average prices.
Laurie Cameron +3 more
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