An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models
Xiaogang Su, Chih‐Ling Tsai · The International Journal of Biostatistics · 2006
We propose an improved Akaike information criterion (AICc) for generalized log-gamma regression models, which include the extreme-value and normal regression models as special cases. Moreover, we extend our proposed criterion to situations when the data contain censored observations. Monte Carlo results show that AICc outperforms the classical Akaike information criterion (AIC), and an empirical example is presented to illustrate its usefulness.