Variable selection in generalized linear models with canonical link functions
AbstractThis 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 71 (2005)
Issue (Month): 4 (March)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- 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.
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