Particle filter-based approximate maximum likelihood inference asymptotics in state-space models

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.

Details

Authors
  • Jimmy Olsson
  • Tobias Rydén
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Probability Theory and Statistics

Keywords

  • state-space model, consistency, maximum likelihood, Particle filter
Original languageEnglish
Title of host publicationESAIM Proceedings
Pages115-120
Number of pages6
Volume19
Publication statusPublished - 2007
Publication categoryResearch
Peer-reviewedYes
EventConference Oxford sur les méthodes de Monte Carlo séquentielles -
Duration: 0001 Jan 2 → …

Publication series

Name
Volume19
ISSN (Print)1270-900X

Conference

ConferenceConference Oxford sur les méthodes de Monte Carlo séquentielles
Period0001/01/02 → …