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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Identifiability conditions for partially-observed
Markov chains - Douc\, R (Telecom SudParis)
DTSTART;TZID=Europe/London:20140423T155000
DTEND;TZID=Europe/London:20140423T162500
UID:TALK52126AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/52126
DESCRIPTION:Co-authors: Franois ROUEFF (LTCI\, UMR 5141\, Tele
com Paristech\, France)\, Tepmony SIM (LTCI\, UMR
5141\, Telecom Paristech\, France) \n\nThis paper
deals with a parametrized family of partially-obse
rved bivariate Markov chains. We establish that th
e limit of the normalized log-likelihood is maximi
zed when the parameter belongs to the equivalence
class of the true parameter\, which is a key featu
re for obtaining consistency of the Maximum Likeli
hood Estimators in well-specified models. A novel
aspect of this work is that geometric ergodicity o
f the Markov chain associated to the complete data
\, or exponential separation on measures are no mo
re needed provided that the invariant distribution
is assumed to be unique\, regardless its rate of
convergence to the equilibrium. The result is pres
ented in a general framework including both fully
dominated or partially dominated models as Hidden
Markov models or Observation-Driven times series o
f counts.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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