Estimating the Null and the Proportion of Non-Null Effects in Large-Scale Multiple Comparisons

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dc.contributor.authorJin, Jiashun
dc.contributor.authorCai, T. Tony
dc.date2023-05-17T15:00:49.000
dc.date.accessioned2023-05-23T03:37:03Z
dc.date.available2023-05-23T03:37:03Z
dc.date.issued2007-01-01
dc.date.submitted2016-07-15T07:11:08-07:00
dc.description.abstractAn important issue raised by Efron in the context of large-scale multiple comparisons is that in many applications, the usual assumption that the null distribution is known is incorrect, and seemingly negligible differences in the null may result in large differences in subsequent studies. This suggests that a careful study of estimation of the null is indispensable. In this article we consider the problem of estimating a null normal distribution, and a closely related problem, estimation of the proportion of nonnull effects. We develop an approach based on the empirical characteristic function and Fourier analysis. The estimators are shown to be uniformly consistent over a wide class of parameters. We investigate the numerical performance of the estimators using both simulated and real data. In particular, we apply our procedure to the analysis of breast cancer and human immunodeficiency virus microarray datasets. The estimators perform favorably compared with existing methods.
dc.identifier.urihttps://repository.upenn.edu/handle/20.500.14332/47894
dc.legacy.articleid1097
dc.legacy.fields10.1198/016214507000000167
dc.legacy.fulltexturlhttps://repository.upenn.edu/cgi/viewcontent.cgi?article=1097&context=statistics_papers&unstamped=1
dc.rights<p>This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 01 Jan 2012, available online: <a href="http://wwww.tandfonline.com/10.1198/016214507000000167">http://wwww.tandfonline.com/10.1198/016214507000000167</a>.</p>
dc.source.beginpage495
dc.source.endpage506
dc.source.issue500
dc.source.issue478
dc.source.journalStatistics Papers
dc.source.journaltitleJournal of the American Statistical Association
dc.source.statuspublished
dc.source.volume102
dc.subject.othercharacteristic functions
dc.subject.otherempirical characteristic function
dc.subject.otherFourier coefficients
dc.subject.othermultiple testing
dc.subject.othernull distribution
dc.subject.otherproportion of nonnull effects
dc.subject.otherStatistics and Probability
dc.titleEstimating the Null and the Proportion of Non-Null Effects in Large-Scale Multiple Comparisons
dc.typeArticle
digcom.contributor.authorJin, Jiashun
digcom.contributor.authorCai, T. Tony
digcom.identifierstatistics_papers/500
digcom.identifier.contextkey8837366
digcom.identifier.submissionpathstatistics_papers/500
digcom.typearticle
dspace.entity.typePublication
upenn.schoolDepartmentCenterStatistics Papers
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