Open Access
October 2020 Minimax optimal sequential hypothesis tests for Markov processes
Michael Fauß, Abdelhak M. Zoubir, H. Vincent Poor
Ann. Statist. 48(5): 2599-2621 (October 2020). DOI: 10.1214/19-AOS1899


Under mild Markov assumptions, sufficient conditions for strict minimax optimality of sequential tests for multiple hypotheses under distributional uncertainty are derived. First, the design of optimal sequential tests for simple hypotheses is revisited, and it is shown that the partial derivatives of the corresponding cost function are closely related to the performance metrics of the underlying sequential test. Second, an implicit characterization of the least favorable distributions for a given testing policy is stated. By combining the results on optimal sequential tests and least favorable distributions, sufficient conditions for a sequential test to be minimax optimal under general distributional uncertainties are obtained. The cost function of the minimax optimal test is further identified as a generalized $f$-dissimilarity and the least favorable distributions as those that are most similar with respect to this dissimilarity. Numerical examples for minimax optimal sequential tests under different uncertainties illustrate the theoretical results.


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Michael Fauß. Abdelhak M. Zoubir. H. Vincent Poor. "Minimax optimal sequential hypothesis tests for Markov processes." Ann. Statist. 48 (5) 2599 - 2621, October 2020.


Received: 1 September 2018; Revised: 1 May 2019; Published: October 2020
First available in Project Euclid: 19 September 2020

MathSciNet: MR4152114
Digital Object Identifier: 10.1214/19-AOS1899

Primary: 62L10
Secondary: 62C20

Keywords: distributional uncertainty , minimax procedures , multiple hypothesis testing , robust hypothesis testing , sequential analysis

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.48 • No. 5 • October 2020
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