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Variable selection in generalized linear models with canonical link functions

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  • Jin, Man
  • Fang, Yixin
  • Zhao, Lincheng
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 71 (2005)
    Issue (Month): 4 (March)
    Pages: 371-382

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    Handle: RePEc:eee:stapro:v:71:y:2005:i:4:p:371-382

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    Related research

    Keywords: Generalized linear model Canonical link function Information theoretic criteria Model selection;

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    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.
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