Nonasymptotic control of the MLE for misspecified nonparametric hidden Markov models

Luc Lehéricy

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arXiv · 1807.03997

Nonasymptotic control of the MLE for misspecified nonparametric hidden Markov models

Luc Lehéricy

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Abstract

Finite state space hidden Markov models are flexible tools to model phenomena with complex time dependencies: any process distribution can be approximated by a hidden Markov model with enough hidden states.We consider the problem of estimating an unknown process distribution using nonparametric hidden Markov models in the misspecified setting, that is when the data-generating process may not be a hidden Markov model.We show that when the true distribution is exponentially mixing and satisfies a forgetting assumption, the maximum likelihood estimator recovers the best approximation of the true distribution. We prove a finite sample bound on the resulting error and show that it is optimal in the minimax sense--up to logarithmic factors--when the model is well specified.

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Luc Lehéricy. 2018-07-11. Nonasymptotic control of the MLE for misspecified nonparametric hidden Markov models. https://arxiv.org/abs/1807.03997

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