The maximizing set of the asymptotic normalized log-likelihood for partially observed Markov chains

DOI10.1214/15-AAP1149zbMath1352.60102arXiv1509.09048OpenAlexW2962922362MaRDI QIDQ341618

Randal Douc, François Roueff, Tepmony Sim

Publication date: 16 November 2016

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1509.09048

zbMATH Keywords

consistencytime seriesergodicityhidden Markov modelsmaximum likelihood estimatorsobservation-driven models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05) Discrete-time Markov processes on general state spaces (60J05)

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Asymptotic analysis of model selection criteria for general hidden Markov models ⋮ General-order observation-driven models: ergodicity and consistency of the maximum likelihood estimator ⋮ Handy sufficient conditions for the convergence of the maximum likelihood estimator in observation-driven models ⋮ Statistical analysis of multivariate discrete-valued time series ⋮ COUNT AND DURATION TIME SERIES WITH EQUAL CONDITIONAL STOCHASTIC AND MEAN ORDERS ⋮ Bayes posterior convergence for loss functions via almost additive thermodynamic formalism

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