Bias-Corrected AIC for Selecting Variables in Poisson Regression Models
Ken‐ichi Kamo, Hirokazu Yanagihara, Kenichi Satoh · Communication in Statistics- Theory and Methods · 2013
In the present article, we consider the variable selection problem in Poisson regression models. Akaike's information criterion (AIC) is the most commonly applied criterion for selecting variables. However, the bias of the AIC cannot be ignored, especially in small samples. We herein propose a new bias-corrected version of the AIC that is constructed by stochastic expansion of the maximum likelihood estimator. The proposed information criterion can reduce the bias of the AIC from O(n−1) to O(n−2). The results of numerical investigations indicate that the proposed criterion is better than the AIC.