IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v33y2006i4p651-671.html
   My bibliography  Save this article

Parameter Estimation in Pair‐hidden Markov Models

Author

Listed:
  • ANA ARRIBAS‐GIL
  • ELISABETH GASSIAT
  • CATHERINE MATIAS

Abstract

. This paper deals with parameter estimation in pair‐hidden Markov models. We first provide a rigorous formalism for these models and discuss possible definitions of likelihoods. The model is biologically motivated and therefore naturally leads to restrictions on the parameter space. Existence of two different information divergence rates is established and a divergence property is shown under additional assumptions. This yields consistency for the parameter in parametrization schemes for which the divergence property holds. Simulations illustrate different cases which are not covered by our results.

Suggested Citation

  • Ana Arribas‐Gil & Elisabeth Gassiat & Catherine Matias, 2006. "Parameter Estimation in Pair‐hidden Markov Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 651-671, December.
  • Handle: RePEc:bla:scjsta:v:33:y:2006:i:4:p:651-671
    DOI: 10.1111/j.1467-9469.2006.00513.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9469.2006.00513.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9469.2006.00513.x?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
    ---><---

    Citations

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


    Cited by:

    1. Arribas-Gil Ana, 2010. "Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-37, January.
    2. Arribas-Gil Ana & Matias Catherine, 2017. "A time warping approach to multiple sequence alignment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 133-144, April.
    3. Arribas-Gil Ana & Matias Catherine, 2012. "A Context Dependent Pair Hidden Markov Model for Statistical Alignment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-29, January.

    More about this item

    Statistics

    Access and download statistics

    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:bla:scjsta:v:33:y:2006:i:4:p:651-671. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.