Title

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

Keywords

AICc; Kullback-Leibler information; Parametric accelerated failure time models; Survival model selection

Abstract

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. Copyright © 2006 The Berkeley Electronic Press. All rights reserved.

Publication Date

1-1-2006

Publication Title

International Journal of Biostatistics

Volume

2

Issue

1

Number of Pages

-

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.2202/1557-4679.1032

Socpus ID

33845271698 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33845271698

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