title: Model selection for extended quasi-likelihood models in small samples.
abstract: We develop a small sample criterion (AICc) for the selection of extended quasi-likelihood models. In contrast to the Akaike information criterion (AIC). AICc provides a more nearly unbiased estimator for the expected Kullback-Leibler information. Consequently, it often selects better models than AIC in small samples. For the logistic regression model, Monte Carlo results show that AICc outperforms AIC, Pregibon's (1979, Data Analytic Methods for Generalized Linear Models. Ph.D. thesis. University of Toronto) Cp*, and the Cp selection criteria of Hosmer et al. (1989, Biometrics 45, 1265-1270). Two examples are presented.
authors.affiliation: Department of Statistics and Operations Research, New York University, New York 10012, USA.
authors.forename: C M
authors.initials: CM
authors.lastname: Hurvich
authors.forename: C L
authors.initials: CL
authors.lastname: Tsai
mesh_terms: D000367:Age Factors; D001699:Biometry; D001706:Biopsy; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D010173:Palpation; D011336:Probability; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000473:pathology / Q000601:surgery; D012044:Regression Analysis; D018401:Sample Size