Estimating the proportion of true null hypotheses in multiple testing problems
type: Item
instance of: scholarly article
title: Estimating the proportion of true null hypotheses in multiple testing problems
zbMATH Open document ID: 1431.62325
DOI: 10.1155/2016/3937056
author: Oluyemi Oyeniran
author: Hanfeng Chen
published in: Journal of Probability and Statistics
publication date: 2018-08-14T00:00:00Z
review text: Summary: The problem of estimating the proportion, \(\pi_0\), of the true null hypotheses in a multiple testing problem is important in cases where large scale parallel hypotheses tests are performed independently. While the problem is a quantity of interest in its own right in applications, the estimate of \(\pi_0\) can be used for assessing or controlling an overall false discovery rate. In this article, we develop an innovative nonparametric maximum likelihood approach to estimate \(\pi_0\). The nonparametric likelihood is proposed to be restricted to multinomial models and an EM algorithm is also developed to approximate the estimate of \(\pi_0\). Simulation studies show that the proposed method outperforms other existing methods. Using experimental microarray datasets, we demonstrate that the new method provides satisfactory estimate in practice.
Mathematics Subject Classification ID: 62J15
Mathematics Subject Classification ID: 62G05
zbMATH DE Number: 6917549
Wikidata QID: Q59129349
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/3937056
OpenAlex ID: W2560240673
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description: scientific article; zbMATH DE number 6917549