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Variable selection in generalized linear models with canonical link functions

Author

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  • 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
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    References listed on IDEAS

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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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