Journal
ANNALS OF STATISTICS
Volume 34, Issue 1, Pages 373-393Publisher
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/009053605000000741
Keywords
hypothesis testing; multiple comparisons; sparsity
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We consider the problem of estimating the number of false null hypotheses among a very large number of independently tested hypotheses, focusing on the situation in which the proportion of false null hypotheses is very small. We propose a family of methods for establishing lower 100(l - alpha)% confidence bounds for this proportion, based on the empirical distribution of the p-values of the tests. Methods in this family are then compared in terms of ability to consistently estimate the proportion by letting alpha -> 0 as the number of hypothesis tests increases and the proportion decreases. This work is motivated by a signal detection problem that occurs in astronomy.
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