Law of iterated logarithm and consistent model selection criterion in logistic regression
In this paper we establish a law of iterated logarithm for the maximum likelihood estimator of the parameters in a logistic regression model with canonical link. This result establishes the strong consistency of some relevant model selection criteria. For a model selection criterion whose objective function consists of a minus log-likelihood like quantity and a penalty term, we have shown that it will select the simplest correct model almost surely if the penalty term is an increasing function of the model dimension and has an order higher than O(loglog n).
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 56 (2002)
Issue (Month): 1 (January)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:56:y:2002:i:1:p:101-112. 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: (Zhang, Lei)
If references are entirely missing, you can add them using this form.