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August 2013 Divergence rates of Markov order estimators and their application to statistical estimation of stationary ergodic processes
Zsolt Talata
Bernoulli 19(3): 846-885 (August 2013). DOI: 10.3150/12-BEJ468

Abstract

Stationary ergodic processes with finite alphabets are estimated by finite memory processes from a sample, an $n$-length realization of the process, where the memory depth of the estimator process is also estimated from the sample using penalized maximum likelihood (PML). Under some assumptions on the continuity rate and the assumption of non-nullness, a rate of convergence in $\bar{d}$-distance is obtained, with explicit constants. The result requires an analysis of the divergence of PML Markov order estimators for not necessarily finite memory processes. This divergence problem is investigated in more generality for three information criteria: the Bayesian information criterion with generalized penalty term yielding the PML, and the normalized maximum likelihood and the Krichevsky–Trofimov code lengths. Lower and upper bounds on the estimated order are obtained. The notion of consistent Markov order estimation is generalized for infinite memory processes using the concept of oracle order estimates, and generalized consistency of the PML Markov order estimator is presented.

Citation

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Zsolt Talata. "Divergence rates of Markov order estimators and their application to statistical estimation of stationary ergodic processes." Bernoulli 19 (3) 846 - 885, August 2013. https://doi.org/10.3150/12-BEJ468

Information

Published: August 2013
First available in Project Euclid: 26 June 2013

zbMATH: 06198871
MathSciNet: MR3079298
Digital Object Identifier: 10.3150/12-BEJ468

Rights: Copyright © 2013 Bernoulli Society for Mathematical Statistics and Probability

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Vol.19 • No. 3 • August 2013
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