Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control

We develop a compound decision theory framework for multiple-testing problems and derive an oracle rule based on the z values that minimizes the false nondiscovery rate (FNR) subject to a constraint on the false discovery rate (FDR). We show that many commonly used multiple-testing procedures, which...

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Vydáno v:	Journal of the American Statistical Association Ročník 102; číslo 479; s. 901 - 912
Hlavní autoři:	Sun, Wenguang, Cai, T. Tony
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Alexandria, VA Taylor & Francis 01.09.2007 American Statistical Association Taylor & Francis Ltd
Témata:	Adaptive procedure Applications Applied statistics Compound decision rule Decision analysis Decision making Decision theory Density estimation Discovery Exact sciences and technology False discovery rate False positive errors General topics HIV Human immunodeficiency virus Hypothesis testing Local false discovery rate Mathematical procedures Mathematics Monotone likelihood ratio Multivariate analysis Null hypothesis Oracles P values Probability and statistics Proportions Rules Sciences and techniques of general use Significance tests Statistical methods Statistical models Statistics Tests Theory and Methods Weighted classification Non parametric estimation Multivariate analysis Microarray Multiple decision Hypothesis test Statistical test Law of large numbers P value Oracle Compound decision rule Human Adaptive procedure Data analysis Retroviridae Statistical estimation Statistical decision Monotone likelihood ratio Lentivirus Decision rule Weighted classification Virus Statistical method Decision theory Local false discovery rate False discovery rate Human immunodeficiency virus Application
ISSN:	0162-1459, 1537-274X
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Shrnutí:	We develop a compound decision theory framework for multiple-testing problems and derive an oracle rule based on the z values that minimizes the false nondiscovery rate (FNR) subject to a constraint on the false discovery rate (FDR). We show that many commonly used multiple-testing procedures, which are p value-based, are inefficient, and propose an adaptive procedure based on the z values. The z value-based adaptive procedure asymptotically attains the performance of the z value oracle procedure and is more efficient than the conventional p value-based methods. We investigate the numerical performance of the adaptive procedure using both simulated and real data. In particular, we demonstrate our method in an analysis of the microarray data from a human immunodeficiency virus study that involves testing a large number of hypotheses simultaneously.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0162-1459 1537-274X
DOI:	10.1198/016214507000000545