EFFECTS OF STATISTICAL DEPENDENCE ON MULTIPLE TESTING UNDER A HIDDEN MARKOV MODEL

• 成果类型: Article
• 署名作者: Chi, Zhiyi
• 署名单位: University of Connecticut
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/10-AOS822
• 发表日期: 2011
• 页码: 439-473
• 关键词: false discovery rate; maximum-likelihood estimator; geometric ergodicity; STABILITY; chains

摘要

The performance of multiple hypothesis testing is known to be affected by the statistical dependence among random variables involved. The mechanisms responsible for this, however, are not well understood. We study the effects of the dependence structure of a finite state hidden Markov model (HMM) on the likelihood ratios critical for optimal multiple testing on the hidden states. Various convergence results are obtained for the likelihood ratios as the observations of the HMM form an increasing long chain. Analytic expansions of the first and second order derivatives are obtained for the case of binary states, explicitly showing the effects of the parameters of the HMM on the likelihood ratios.