GMM-based procedure for multiple hypotheses testing

被引:0
|
作者
Zhang, Jingyi [1 ]
He, Zhijian [1 ]
机构
[1] South China Univ Technol, Sch Math, Guangzhou 610641, Peoples R China
基金
美国国家科学基金会;
关键词
False discovery rate; False nondiscovery rate; Gaussian mixture model; Multiple hypotheses; FALSE DISCOVERY RATE; NULL;
D O I
10.1080/03610918.2022.2082476
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Multiple hypotheses testing has been widely studied in the literature due to its broad applicability, particularly in the fields of biogenetics and astrogeology. The false discovery rate (FDR) is a useful error control criterion for large-scale multiple hypotheses, which is loosely defined as the expected proportion of false positives among all rejected hypotheses. In this paper, we propose a Gaussian mixture model (GMM) to fit the distribution of the Z-value statistics, including the nulls distribution as a fixed component. The nulls proportion and the real nulls distribution are estimated by the fitted GMM simultaneously. A GMM-based procedure is then proposed to minimize the false nondiscovery rate (FNR) subject to a constraint on the FDR. Both simulations and real data analysis show that the GMM-based procedure performs considerably well comparing to some competitors.
引用
收藏
页码:2605 / 2623
页数:19
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