Estimating False Discovery Proportion Under Arbitrary Covariance Dependence

Multiple hypothesis testing is a fundamental problem in high-dimensional inference, with wide applications in many scientific fields. In genome-wide association studies, tens of thousands of tests are performed simultaneously to find if any single-nucleotide polymorphisms (SNPs) are associated with...

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Vydáno v:	Journal of the American Statistical Association Ročník 107; číslo 499; s. 1019 - 1035
Hlavní autoři:	Fan, Jianqing, Han, Xu, Gu, Weijie
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Taylor & Francis Group 01.09.2012
Témata:	Applied statistics Arbitrary dependence structure Correlations Covariance Covariance matrices Estimate reliability Estimation methods Estimators False discovery rate Genome-wide association studies genome-wide association study High-dimensional inference Multiple hypothesis testing Null hypothesis Random variables single nucleotide polymorphism Statistical variance Theory and Methods arbitrary dependence structure Multiple hypothesis testing false discovery rate genome-wide association studies high dimensional inference
ISSN:	1537-274X, 0162-1459, 1537-274X
On-line přístup:	Získat plný text
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