IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v71y2005i4p371-382.html
   My bibliography  Save this article

Variable selection in generalized linear models with canonical link functions

Author

Listed:
  • Jin, Man
  • Fang, Yixin
  • Zhao, Lincheng

Abstract

This paper studies a class of AIC-like model selection criteria for a generalized linear model with the canonical link. They have the form of , where is the maximized log-likelihood, p is the number of parameters and C is a term depending on the sample size n and satisfying C/n-->0 and as n-->[infinity]. Under suitable conditions, this class of criteria is shown to be strongly consistent. A simulation study was also conducted to assess the finite-sample performance with various choices of C for variable selection in a logit model and a log-linear model.

Suggested Citation

  • Jin, Man & Fang, Yixin & Zhao, Lincheng, 2005. "Variable selection in generalized linear models with canonical link functions," Statistics & Probability Letters, Elsevier, vol. 71(4), pages 371-382, March.
  • Handle: RePEc:eee:stapro:v:71:y:2005:i:4:p:371-382
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(05)00011-8
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Qian, Guoqi & Field, Chris, 2002. "Law of iterated logarithm and consistent model selection criterion in logistic regression," Statistics & Probability Letters, Elsevier, vol. 56(1), pages 101-112, January.
    Full references (including those not matched with items on IDEAS)

    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:stapro:v:71:y:2005:i:4:p:371-382. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.