IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v5y2006i1n8.html
   My bibliography  Save this article

A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity

Author

Listed:
  • Dalevi Daniel

    (Chalmers University of Technology)

  • Dubhashi Devdatt

    (Chalmers University of Technology)

  • Hermansson Malte

    (Gothenburg University)

Abstract

Recently Peres and Shields discovered a new method for estimating the order of a stationary fixed order Markov chain. They showed that the estimator is consistent by proving a threshold result. While this threshold is valid asymptotically in the limit, it is not very useful for DNA sequence analysis where data sizes are moderate. In this paper we give a novel interpretation of the Peres-Shields estimator as a sharp transition phenomenon. This yields a precise and powerful estimator that quickly identifies the core dependencies in data. We show that it compares favorably to other estimators, especially in the presence of variable dependencies. Motivated by this last point, we extend the Peres-Shields estimator to Variable Length Markov Chains. We compare it to a well-established estimator and show that it is superior in terms of the predictive likelihood. We give an application to the problem of detecting DNA sequence similarity in plasmids.

Suggested Citation

  • Dalevi Daniel & Dubhashi Devdatt & Hermansson Malte, 2006. "A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-26, March.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:8
    DOI: 10.2202/1544-6115.1214
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1214
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1214?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. Papapetrou, M. & Kugiumtzis, D., 2013. "Markov chain order estimation with conditional mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1593-1601.

    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:bpj:sagmbi:v:5:y:2006:i:1:n:8. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.