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

• Published in: Computational statistics & data analysis Vol. 23; no. 3; pp. 373 - 379
• Main Authors: Jovanovic, B.D., Hosmer, D.W.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 1997; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Likelihood-ratio; Linearization; Pseudo data; Score statistic; Working vector; Working vector; Likelihood-ratio; Pseudo data; Score statistic; Linearization
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/S0167-9473(96)00035-7

We evaluate performance of Mallows'
C
p
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
C
p
is compatible with that of LR and S.
C
p
rejects both the correct and incorrect models at least as often as the other two statistics. Thus,
C
p
appears to be a conservative selection criterion in the sense that it has lower sensitivity but higher specificity than LR or S.