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SUMMARY:Asymptotic Properties of Recursive Maximum Likelihood Estimation i
 n State-Space Models - Tadic\, V (University of Bristol)
DTSTART:20140425T134500Z
DTEND:20140425T141500Z
UID:TALK52185@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-author: Arnaud Doucet (University of Oxford) \n\nRecursive 
 maximum likelihood algorithm for state-space models (i.e.\, for continuous
  state hidden Markov models) is an iterative estimation method based on pa
 rticle filter and stochastic gradient search. In this talk\, resent result
 s on its asymptotic properties are presented. These results are focused on
  the asymptotic bias and the asymptotic variance. They also involve diffus
 ion approximation\, almost-sure and mean-square convergence of the recursi
 ve maximum likelihood algorithm. Some auxiliary (yet\, rather interesting)
  results on the asymptotic properties of the particle filter and the log-l
 ikelihood are presented in the talk\, too. \n\n
LOCATION:Seminar Room 1\, Newton Institute
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