Effects of statistical dependence on multiple testing under a hidden Markov model
The Annals of Statistics · 2011 · Volume 39 · Issue 1
Abstract
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.
Keywords
62H15, 62M02, contraction, FDR, HMM, multiple hypothesis testing, Nonlinear filtering
Enable JavaScript for citation tools and additional paper interactions.