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Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach

Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat; 23(94):1−57, 2022.

Abstract

Given a nonparametric Hidden Markov Model (HMM) with two states, the question of constructing efficient multiple testing procedures is considered, treating the states as unknown null and alternative hypotheses. A procedure is introduced, based on nonparametric empirical Bayes ideas, that controls the False Discovery Rate (FDR) at a user-specified level. Guarantees on power are also provided, in the form of a control of the true positive rate. One of the key steps in the construction requires supremum-norm convergence of preliminary estimators of the emission densities of the HMM. We provide the existence of such estimators, with convergence at the optimal minimax rate, for the case of a HMM with $J\ge 2$ states, which is of independent interest.

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