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ESAIM: Proc., 2007, Vol. 19, pp. 115-120
DOI: 10.1051/proc:071915
Particle filter-based approximate maximum likelihood inference asymptotics in state-space models
Jimmy Olsson and Tobias RydénCentre for Mathematical Sciences, Lund University, Box 118, 221 00 Lund, Sweden
(Published online: 30 October 2007)
Abstract
To implement maximum likelihood estimation in
state-space models, the log-likelihood function must be approximated.
We study such approximations based on particle filters, and in
particular conditions for consistency of the corresponding
approximate maximum likelihood estimator. Numerical results illustrate
the theory.
Mathematics Subject Classification. 62M09, 62F12
Key words: Particle filter, state-space model, maximum likelihood, consistency
© EDP Sciences, ESAIM 2007
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