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

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.

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Year of publication:	2006
Authors:	Su, Xiaogang ; Tsai, Chih-Ling
Published in:	International Journal of Biostatistics. - Berkeley Electronic Press. - Vol. 2.2006, 1, p. 1032-1032
Publisher:	Berkeley Electronic Press
Subject:	parametric accelerated failure time models \| AICc \| Kullback-Leibler information \| survival model selection

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Extent:	application/pdf
Type of publication:	Article
Notes:	oai:bepress:ijb-1032
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005752609