A Kalman particle filter for online parameter estimation with applications to affine models

In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous-time state space model with discrete-time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two...

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Veröffentlicht in:	Statistical inference for stochastic processes : an international journal devoted to time series analysis and the statistics of continuous time processes and dynamic systems Jg. 24; H. 2; S. 353 - 403
Hauptverfasser:	He, Jian, Khedher, Asma, Spreij, Peter
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.07.2021 Springer Nature B.V
Schlagworte:	Algorithms Computing time Convergence Kalman filters Mathematics Mathematics and Statistics Parameter estimation Probability Theory and Stochastic Processes State space models State variable Statistical Theory and Methods 93E11 Parameter estimation 65C35 Affine process Kalman filter State space model 62P05 Particle filter Posterior distribution
ISSN:	1387-0874, 1572-9311
Online-Zugang:	Volltext
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