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Publicly Available Published by De Gruyter November 25, 2006

An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models

Xiaogang Su and Chih-Ling Tsai

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.

Published Online: 2006-11-25

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