Title :
Parameter estimation in a general state space model from short observation data: A SMC based approach
Author :
Saha, S. ; Mandal, P.K. ; Bagchi, A. ; Boers, Y. ; Driessen, H.
Author_Institution :
Dept. Of Appl. Math., Univ. of Twente, Netherlands
Abstract :
In this article, we propose a SMC based method for estimating the static parameter of a general state space model. The proposed method is based on maximizing the joint likelihood of the observation and unknown state sequence with respect to both the unknown parameters and the unknown state sequence. This in turn, casts the problem into simultaneous estimations of state and parameter. We show the efficacy of this method by numerical simulation results.
Keywords :
Monte Carlo methods; maximum likelihood estimation; sequential Monte Carlo method; state space model; static parameter estimation; Mathematical model; Mathematics; Maximum likelihood estimation; Monte Carlo methods; Numerical simulation; Parameter estimation; Particle filters; Sliding mode control; State estimation; State-space methods; parameter estimation; particle filter; sequential Monte Carlo;
Conference_Titel :
Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Conference_Location :
Cardiff
Print_ISBN :
978-1-4244-2709-3
Electronic_ISBN :
978-1-4244-2711-6
DOI :
10.1109/SSP.2009.5278643
