IDEAS home Printed from
   My bibliography  Save this article

Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment


  • Arribas-Gil Ana

    (Universidad Carlos III de Madrid)


In this work we deal with parameter estimation in a latent variable model, namely the multiple-hidden i.i.d. model, which is derived from multiple alignment algorithms. We first provide a rigorous formalism for the homology structure of k sequences related by a star-shaped phylogenetic tree in the context of multiple alignment based on indel evolution models. We discuss possible definitions of likelihoods and compare them to the criterion used in multiple alignment algorithms. Existence of two different Information divergence rates is established and a divergence property is shown under additional assumptions. This would yield consistency for the parameter in parametrization schemes for which the divergence property holds. We finally extend the definition of the multiple-hidden i.i.d. model and the results obtained to the case in which the sequences are related by an arbitrary phylogenetic tree. Simulations illustrate different cases which are not covered by our results.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:10

    Download full text from publisher

    File URL:
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

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

    References listed on IDEAS

    1. 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.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. repec:bpj:sagmbi:v:16:y:2017:i:2:p:133-144:n:2 is not listed on IDEAS

    More about this item


    Access and download statistics


    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:bpj:sagmbi:v:9:y:2010:i:1:n:10. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla). General contact details of provider: .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.