Parameter estimation in general state-space models

A Doucet, VB Tadic

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Particle filtering techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. If the model includes fixed parameters, a standard technique to perform parameter estimation consists of extending the state with the parameter to transform the problem into an optimal filtering problem. However, this approach requires the use of special particle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and gradient algorithms to perform batch and recursive maximum likelihood parameter estimation. An original particle method is presented to implement these approaches and their efficiency is assessed through simulation.
Translated title of the contributionParameter estimation in general state-space models
Original languageEnglish
Pages (from-to)409 - 422
Number of pages14
JournalAnnals of the Institute of Statistical Mathematics
Volume55 (2)
DOIs
Publication statusPublished - Jun 2003

Bibliographical note

Publisher: Kluwer Academic Publ
Other identifier: IDS number 700XH

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