IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v135y2015icp25-42.html
   My bibliography  Save this article

MDR method for nonbinary response variable

Author

Listed:
  • Bulinski, Alexander
  • Rakitko, Alexander

Abstract

For nonbinary response variable depending on a finite collection of factors with values in a finite subset of R the problem of the optimal forecast is considered. The quality of prediction is described by the error function involving a penalty function. The criterion of almost sure convergence to unknown error function for proposed estimates constructed by means of a prediction algorithm and K-fold cross-validation procedure is established. It is demonstrated that imposed conditions admit the efficient verification. The developed approach permits to realize the dimensionality reduction of factors under consideration. One can see that the results obtained provide the base to identify the set of significant factors. Such problem arises, e.g., in medicine and biology. The central limit theorem for proposed statistics is proven as well. In this way one can indicate the approximate confidence intervals for employed error function.

Suggested Citation

  • Bulinski, Alexander & Rakitko, Alexander, 2015. "MDR method for nonbinary response variable," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 25-42.
    • Handle: RePEc:eee:jmvana:v:135:y:2015:i:c:p:25-42
    DOI: 10.1016/j.jmva.2014.11.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X14002632
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2014.11.008?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.

    References listed on IDEAS

    as
    1. Park, Junyong, 2009. "Independent rule in classification of multivariate binary data," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2270-2286, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Park, Junyong & Park, DoHwan, 2015. "Stein’s method in high dimensional classification and applications," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 110-125.
    2. Junyong Park, 2019. "Testing homogeneity of proportions from sparse binomial data with a large number of groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 505-535, June.

    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:jmvana:v:135:y:2015:i:c:p:25-42. 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.

    If CitEc recognized a bibliographic 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.

    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/622892/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.