CUED Publications database

Asymptotic analysis of model selection criteria for general hidden Markov models

Yonekura, S and Beskos, A and Singh, SS (2021) Asymptotic analysis of model selection criteria for general hidden Markov models. Stochastic Processes and their Applications, 132. pp. 164-191. ISSN 0304-4149

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Abstract

The paper obtains analytical results for the asymptotic properties of Model Selection Criteria – widely used in practice – for a general family of hidden Markov models (HMMs), thereby substantially extending the related theory beyond typical ‘i.i.d.-like’ model structures and filling in an important gap in the relevant literature. In particular, we look at the Bayesian and Akaike Information Criteria (BIC and AIC) and the model evidence. In the setting of nested classes of models, we prove that BIC and the evidence are strongly consistent for HMMs (under regularity conditions), whereas AIC is not weakly consistent. Numerical experiments support our theoretical results.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 06 Dec 2020 00:42
Last Modified: 13 Apr 2021 10:16
DOI: 10.1016/j.spa.2020.10.006