Hurvich C M, Tsai C L
Department of Statistics and Operations Research, New York University, New York 10012, USA.
Biometrics. 1995 Sep;51(3):1077-84.
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.
我们开发了一种用于选择扩展拟似然模型的小样本准则（AICc）。与赤池信息准则（AIC）不同，AICc为期望的库尔贝克-莱布勒信息提供了一个更接近无偏的估计量。因此，在小样本中它通常比AIC能选择出更好的模型。对于逻辑回归模型，蒙特卡罗结果表明AICc优于AIC、普雷吉本（1979年，《广义线性模型的数据分析方法》，博士论文，多伦多大学）的Cp*以及霍斯默等人（1989年，《生物统计学》45卷，1265 - 1270页）的Cp选择准则。给出了两个例子。
