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Article Dans Une Revue Journal of Statistical Planning and Inference Année : 2009

A minimum description length approach to hidden Markov models with Poisson and Gaussian emissions. Application to order identification

Résumé

We address the issue of order identification for hidden Markov models with Poisson and Gaussian emissions. We prove information-theoretic BIC-like mixture inequalities in the spirit of [Finesso, 1991; Liu and Narayan, 1994; Gassiat and Boucheron, 2003]. These inequalities lead to consistent penalized estimators that need no prior bound on the order. A simulation study and an application to postural analysis in humans are provided.

Dates et versions

hal-03936426 , version 1 (12-01-2023)

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A. Chambaz, A. Garivier, E. Gassiat. A minimum description length approach to hidden Markov models with Poisson and Gaussian emissions. Application to order identification. Journal of Statistical Planning and Inference, 2009, 139 (3), pp.962-977. ⟨10.1016/j.jspi.2008.06.010⟩. ⟨hal-03936426⟩
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