IDEAS home Printed from
   My bibliography  Save this article

Statistical classification with missing covariates


  • Majid Mojirsheibani
  • Zahra Montazeri


Some results related to statistical classification in the presence of missing covariates are presented. We derive representations for the best (Bayes) classifier when some of the covariates can be missing; this is done without imposing any assumptions on the underlying missing probability mechanism. Furthermore, without assuming any missingness-at-random type of conditions, we also construct Bayes consistent classifiers that do not require any imputation-based techniques. Both parametric and non-parametric situations are considered but the emphasis is on the latter. In addition to simple missingness patterns, we also consider the full "Swiss cheese" model, where the missing covariates can be anywhere. Both mechanics and the theoretical validity of our results are discussed. Copyright 2007 Royal Statistical Society.

Suggested Citation

  • Majid Mojirsheibani & Zahra Montazeri, 2007. "Statistical classification with missing covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 839-857.
  • Handle: RePEc:bla:jorssb:v:69:y:2007:i:5:p:839-857

    Download full text from publisher

    File URL:
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

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

    References listed on IDEAS

    1. Hazelton, Martin L., 2000. "Marginal density estimation from incomplete bivariate data," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 75-84, March.
    2. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    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. Majid Mojirsheibani, 2012. "Some results on classifier selection with missing covariates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(4), pages 521-539, May.

    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:bla:jorssb:v:69:y:2007:i:5:p:839-857. 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: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). 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.