On Particle Methods for Parameter Estimation in State-Space Models - Centre de Recherche en Économie et Statistique
Article Dans Une Revue Statistical Science Année : 2015

On Particle Methods for Parameter Estimation in State-Space Models

Nikolas Kantas
  • Fonction : Auteur
Arnaud Doucet
  • Fonction : Auteur
Sumeetpal Singh
  • Fonction : Auteur
Jan Maciejowski
  • Fonction : Auteur

Résumé

Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. However, in most applications, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard particle methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models. We discuss the advantages and limitations of these methods and illustrate their performance on simple models.
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Dates et versions

hal-04793401 , version 1 (06-01-2025)

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Nikolas Kantas, Arnaud Doucet, Sumeetpal Singh, Jan Maciejowski, Nicolas Chopin. On Particle Methods for Parameter Estimation in State-Space Models. Statistical Science, 2015, 30 (3), ⟨10.1214/14-STS511⟩. ⟨hal-04793401⟩
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