Hidden Markov model likelihoods and their derivatives behave like i. i. d. ones. (La vraisemblance des chaînes de Markov cachées se comporte comme celle des variables i. i. d.)

DOI10.1016/S0246-0203(02)01134-2zbMath1011.62087OpenAlexW1724732825MaRDI QIDQ1863423

Tobias Rydén, Ya'acov Ritov, Peter J. Bickel

Publication date: 11 March 2003

Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)

Full work available at URL: http://www.numdam.org/item?id=AIHPB_2002__38_6_825_0

zbMATH Keywords

asymptotic normalityincomplete dataasymptotic expansionshidden Markov modelsmissing datahigher order asymptotics

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Non-Markovian processes: estimation (62M09) Markov processes: estimation; hidden Markov models (62M05)

Related Items (5)

A variance component test for mixed hidden Markov models ⋮ From the early days of the semiparametric field: a conversation with Ya'acov Ritov ⋮ Likelihood Ratio Testing for Hidden Markov Models Under Non‐standard Conditions ⋮ Quasi-hidden Markov model and its applications in change-point problems ⋮ Direct maximization of the likelihood of a hidden Markov model

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