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August 2015 On Particle Methods for Parameter Estimation in State-Space Models
Nikolas Kantas, Arnaud Doucet, Sumeetpal S. Singh, Jan Maciejowski, Nicolas Chopin
Statist. Sci. 30(3): 328-351 (August 2015). DOI: 10.1214/14-STS511


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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Nikolas Kantas. Arnaud Doucet. Sumeetpal S. Singh. Jan Maciejowski. Nicolas Chopin. "On Particle Methods for Parameter Estimation in State-Space Models." Statist. Sci. 30 (3) 328 - 351, August 2015.


Published: August 2015
First available in Project Euclid: 10 August 2015

zbMATH: 1332.62096
MathSciNet: MR3383884
Digital Object Identifier: 10.1214/14-STS511

Keywords: Bayesian inference , maximum likelihood inference , particle filtering , sequential Monte Carlo , state-space models

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.30 • No. 3 • August 2015
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