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: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Alexandria, VA
Taylor & Francis
01.09.2007
American Statistical Association Taylor & Francis Ltd |
| Témata: | |
| ISSN: | 0162-1459, 1537-274X |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. We develop a compound decision theory framework for multiple-testing problems and derive an oracle rule based on the 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. [PUBLICATION ABSTRACT] |
| Author | Sun, Wenguang Cai, T. Tony |
| Author_xml | – sequence: 1 givenname: Wenguang surname: Sun fullname: Sun, Wenguang – sequence: 2 givenname: T. Tony surname: Cai fullname: Cai, T. Tony |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control |
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