Detection and identification of changes of hidden Markov chains: asymptotic theory

Savas Dayanik () and Kazutoshi Yamazaki ()
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Savas Dayanik: Bilkent University
Kazutoshi Yamazaki: Kansai University

Statistical Inference for Stochastic Processes, 2022, vol. 25, issue 2, No 3, 301 pages

Abstract: Abstract This paper revisits a unified framework of sequential change-point detection and hypothesis testing modeled using hidden Markov chains and develops its asymptotic theory. Given a sequence of observations whose distributions are dependent on a hidden Markov chain, the objective is to quickly detect critical events, modeled by the first time the Markov chain leaves a specific set of states, and to accurately identify the class of states that the Markov chain enters. We propose computationally tractable sequential detection and identification strategies and obtain sufficient conditions for the asymptotic optimality in two Bayesian formulations. Numerical examples are provided to confirm the asymptotic optimality.

Keywords: Hypothesis testing; Change point detection; Optimal stopping; Asymptotic optimality; Hidden Markov models; 62L10; 62L15; 62C10; 60G40 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11203-021-09253-5

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