Volume 19, 2007Conference Oxford sur les méthodes de Monte Carlo séquentielles
|Page(s)||115 - 120|
|Published online||30 October 2007|
Particle filter-based approximate maximum likelihood inference asymptotics in state-space models
Centre for Mathematical Sciences, Lund University, Box 118, 221 00 Lund, Sweden
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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