Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime

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dc.creatorDouc, Randal
dc.creatorMoulines, Eric
dc.creatorRyden, Tobias
dc.date2005-03-29
dc.date.accessioned2026-07-07T08:06:47Z
dc.date.available2026-07-07T08:06:47Z
dc.descriptionAn autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053604000000021 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0503681
dc.identifierhttp://arxiv.org/abs/math/0503681
dc.identifierAnnals of Statistics 2004, Vol. 32, No. 5, 2254-2304
dc.identifierdoi:10.1214/009053604000000021
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130729
dc.subjectStatistics Theory
dc.subject62M09 (Primary) 62F12. (Secondary)
dc.titleAsymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
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