IDEAS home Printed from https://ideas.repec.org/a/eee/spapps/v33y1989i2p245-274.html
   My bibliography  Save this article

MLE for partially observed diffusions: direct maximization vs. the em algorithm

Author

Listed:
  • Campillo, Fabien
  • Le Gland, François

Abstract

Two algorithms are compared for maximizing the likelihood function associated with parameter estimation in partially observed diffusion processes: - - the EM algorithm, investigated by Dembo and Zeitouni (1986), an iterative algorithm where, at each iteration, an auxiliary function is computed and maximized; - - the direct approach where the likelihood function itself is computed and maximized. This yields to a comparison of nonlinear smoothing and nonlinear filtering for computing a class of conditional expectations related to the problem of estimation. In particular, it is shown that smoothing is indeed necessary for the EM algorithm approach to be efficient. Time discretization schemes for the stochastic PDE's involved in the algorithms are given, and the link with the discrete time case (hidden Markov model) is explored. Numerical results are presented with the conclusion that direct maximization should be preferred whenever some noise covariances associated with the parameters to be estimated are small.

Suggested Citation

  • Campillo, Fabien & Le Gland, François, 1989. "MLE for partially observed diffusions: direct maximization vs. the em algorithm," Stochastic Processes and their Applications, Elsevier, vol. 33(2), pages 245-274, December.
    • Handle: RePEc:eee:spapps:v:33:y:1989:i:2:p:245-274
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0304-4149(89)90041-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Turner, Rolf, 2008. "Direct maximization of the likelihood of a hidden Markov model," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4147-4160, May.

    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:spapps:v:33:y:1989:i:2:p:245-274. 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.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description .

    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.