Simultaneous Testing of Grouped Hypotheses: Finding Needles in Multiple Haystacks

In large-scale multiple testing problems, data are often collected from heterogeneous sources and hypotheses form into groups that exhibit different characteristics. Conventional approaches, including the pooled and separate analyses, fail to efficiently utilize the external grouping information. We...

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Vydané v:	Journal of the American Statistical Association Ročník 104; číslo 488; s. 1467 - 1481
Hlavní autori:	Cai, T. Tony, Sun, Wenguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria, VA Taylor & Francis 01.12.2009 American Statistical Association Assoc Taylor & Francis Ltd
Predmet:	Applications Applied statistics Compound decision problem Conditional local false discovery rate Consistent estimators Data Data collection Decision making Decision theory Error Estimation Exact sciences and technology Exchangeability Exploration False discovery rate False information False positive errors General topics Group size Grouped hypotheses Heterogeneity Hypotheses Hypothesis testing Inference Information Large-scale multiple testing Mathematical procedures Mathematics Multiple criteria decision making Nonparametric inference Oracles Parametric inference Probability and statistics Sampling Sciences and techniques of general use Simulation Small schools Statistical methods Statistical theories Statistics Tests Theory and Methods Discriminant analysis Data analysis Conditional distribution Grouped data Asymptotic optimality Non parametric estimation Statistical estimation Statistical decision Multivariate analysis Statistical simulation Multiple decision Statistical method Hypothesis test Statistical test Compound decision problem Decision theory Grouped hypotheses Large-scale multiple testing False discovery rate Oracle Application Conditional local false discovery rate Exchangeability
ISSN:	0162-1459, 1537-274X
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