Parameter estimation in a general state space model from short observation data: A SMC based approach

S. Saha, Pranab K. Mandal, Arunabha Bagchi, Y. Boers, H. Driessen

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    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.
    Original languageUndefined
    Title of host publicationProceedings of the 2009 IEEE/SP 15th Workshop on Statistical Signal Processing
    PublisherIEEE
    Pages41-44
    Number of pages4
    ISBN (Print)978-1-4244-2710-9
    DOIs
    Publication statusPublished - 31 Aug 2009

    Publication series

    Name
    PublisherIEEE
    NumberIEEE catal

    Keywords

    • IR-68154
    • METIS-264050
    • Particle filter
    • MSC-11K45
    • Sequential Monte Carlo
    • EWI-16114
    • Parameter estimation

    Cite this

    Saha, S., Mandal, P. K., Bagchi, A., Boers, Y., & Driessen, H. (2009). Parameter estimation in a general state space model from short observation data: A SMC based approach. In Proceedings of the 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (pp. 41-44). [10.1109/SSP.2009.5278643] IEEE. https://doi.org/10.1109/SSP.2009.5278643