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

Su Xiaogang and Tsai Chih-Ling
Additional contact information
Su Xiaogang: University of Central Florida
Tsai Chih-Ling: University of California at Davis

The International Journal of Biostatistics, 2006, vol. 2, issue 1, 22

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.

Keywords: parametric accelerated failure time models; AICc; Kullback-Leibler information; survival model selection (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1557-4679.1032 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:2:y:2006:i:1:n:10

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.2202/1557-4679.1032

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().