IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v79y2009i5p1641-1657.html
   My bibliography  Save this article

Maximum likelihood estimation via the extended covariance and combined square-root filters

Author

Listed:
  • Kulikova, M.V.

Abstract

The method of maximum likelihood is a general method for parameter estimation and is often used in system identification. To implement it, it is necessary to maximize the likelihood function, which is usually done using the gradient approach. It involves the computation of the likelihood gradient with respect to unknown system parameters. For linear stochastic system models this leads to the implementation of the Kalman filter, which is known to be numerically unstable. The aim of this work is to present new efficient algorithms for likelihood gradient evaluation. They are more reliable in practice and improve robustness of computations against roundoff errors. All algorithms are derived in measurement and time updates form. The comparison with the conventional Kalman filter approach and results of numerical experiments are given.

Suggested Citation

  • Kulikova, M.V., 2009. "Maximum likelihood estimation via the extended covariance and combined square-root filters," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1641-1657.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:5:p:1641-1657
    DOI: 10.1016/j.matcom.2008.08.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037847540800284X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2008.08.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kulikova, M.V. & Tsyganova, J.V., 2016. "A unified square-root approach for the score and Fisher information matrix computation in linear dynamic systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 128-141.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:79:y:2009:i:5:p:1641-1657. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.