A particle implementation of the recursive MLE for partially observed diffusions

Abstract : In this paper, the problem of identifying a hidden Markov model (HMM) with general state space, e.g. a partially observed diffusion process, is considered. A particle implementation of the recursive maximum likelihood estimator for a parameter in the transition kernel of the Markov chain is presented. The key assumption is that the derivative of the transition kernel w.r.t. the parameter has a probabilistic interpretation, suitable for Monte Carlo simulation. Examples are given to show that this assumption is satisfied in quite general situations. As a result, the linear tangent filter, i.e. the derivative of the filter w.r.t. the parameter, is absolutely continuous w.r.t. the filter and the idea is to jointly approximate the (prediction) filter and its derivative with the empirical probability distribution and with a weighted empirical distribution associated with the same and unique particle system. Application to the identification of a stochastic volatility model is presented.
Type de document :
Communication dans un congrès
Proceedings of the 13th Symposium on System Identification (SYSID), Rotterdam, Aug 2003, Rotterdam, Netherlands. pp.1305--1310, 2003
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https://hal.inria.fr/hal-00912051
Contributeur : Francois Le Gland <>
Soumis le : jeudi 19 décembre 2013 - 17:42:20
Dernière modification le : mercredi 16 mai 2018 - 11:23:05

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  • HAL Id : hal-00912051, version 1

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Arnaud Guyader, François Le Gland, Nadia Oudjane. A particle implementation of the recursive MLE for partially observed diffusions. Proceedings of the 13th Symposium on System Identification (SYSID), Rotterdam, Aug 2003, Rotterdam, Netherlands. pp.1305--1310, 2003. 〈hal-00912051〉

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