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

Olsson, Jimmy LU and Rydén, Tobias LU (2007) Conference Oxford sur les méthodes de Monte Carlo séquentielles In ESAIM Proceedings 19. p.115-120
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.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
state-space model, consistency, maximum likelihood, Particle filter
in
ESAIM Proceedings
volume
19
pages
6 pages
conference name
Conference Oxford sur les méthodes de Monte Carlo séquentielles
ISSN
1270-900X
DOI
10.1051/proc:071915
language
English
LU publication?
yes
id
3bc932bd-e0b0-4172-a4ac-45dcad6e1b4d (old id 1271962)
date added to LUP
2009-06-03 17:26:14
date last changed
2016-04-16 06:21:11
@misc{3bc932bd-e0b0-4172-a4ac-45dcad6e1b4d,
  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.},
  author       = {Olsson, Jimmy and Rydén, Tobias},
  issn         = {1270-900X},
  keyword      = {state-space model,consistency,maximum likelihood,Particle filter},
  language     = {eng},
  pages        = {115--120},
  series       = {ESAIM Proceedings},
  title        = {Particle filter-based approximate maximum likelihood inference asymptotics in state-space models},
  url          = {http://dx.doi.org/10.1051/proc:071915},
  volume       = {19},
  year         = {2007},
}