Abstract
An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for use of an empirical alternative hypothesis to increase by 50% or more the number of true positives identified at a given significance level.
Disciplines
Bioinformatics | Computational Biology | Microarrays | Statistical Methodology | Statistical Theory
Suggested Citation
Signorovitch, James E., "Multiple Testing With an Empirical Alternative Hypothesis" (November 2006). Harvard University Biostatistics Working Paper Series. Working Paper 60.
https://biostats.bepress.com/harvardbiostat/paper60
Included in
Bioinformatics Commons, Computational Biology Commons, Microarrays Commons, Statistical Methodology Commons, Statistical Theory Commons