A simulation of the performance of Cp in model selection for logistic and Poisson regression

B. D. Jovanovic*, D. W. Hosmer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We evaluate performance of Mallows' Cp in best subsets selection for logistic and Poisson regression models using computer simulations. Comparison of success rate in recognizing correct and incorrect models is made with the likelihood ratio (LR) statistic and with the score statistic (S). We find that performance of Cp is compatible with that of LR and S. Cp rejects both the correct and incorrect models at least as often as the other two statistics. Thus, Cp appears to be a conservative selection criterion in the sense that it has lower sensitivity but higher specificity than LR or S.

Original languageEnglish (US)
Pages (from-to)373-379
Number of pages7
JournalComputational Statistics and Data Analysis
Volume23
Issue number3
DOIs
StatePublished - Jan 9 1997

Keywords

  • Likelihood-ratio
  • Linearization
  • Pseudo data
  • Score statistic
  • Working vector

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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