Variable selection in generalized linear models with canonical link functions

Man Jin, Yixin Fang, Lincheng Zhao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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 log L - p * C, where log L 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 C/log log n → ∞ as n → ∞. 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.

Original languageEnglish (US)
Pages (from-to)371-382
Number of pages12
JournalStatistics and Probability Letters
Volume71
Issue number4
DOIs
StatePublished - Mar 15 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Canonical link function
  • Generalized linear model
  • Information theoretic criteria
  • Model selection

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