A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models
2014 | journal article; research paper. A publication with affiliation to the University of Göttingen.
Jump to: Cite & Linked | Documents & Media | Details | Version history
Documents & Media
euclid.ejs.1393510264.pdf260.43 kBUnknownSuppl A9.08 MBUnknownSuppl B9.08 MBUnknowndocument.pdf314 kBAdobe PDF
Details
- Authors
- Saefken, Benjamin; Kneib, Thomas ; van Waveren, Clara-Sophie; Greven, Sonja
- Abstract
- The conditional Akaike information criterion, AIC, has been frequently used for model selection in linear mixed models. We develop a general framework for the calculation of the conditional AIC for different exponential family distributions. This unified framework incorporates the conditional AIC for the Gaussian case, gives a new justification for Poisson distributed data and yields a new conditional AIC for exponentially dis- tributed responses but cannot be applied to the binomial and gamma distri- butions. The proposed conditional Akaike information criteria are unbiased for finite samples, do not rely on a particular estimation method and do not assume that the variance-covariance matrix of the random effects is known. The theoretical results are investigated in a simulation study. The practical use of the method is illustrated by application to a data set on tree growth.
- Issue Date
- 2014
- Journal
- Electronic Journal of Statistics
- Organization
- Fakultät für Forstwissenschaften und Waldökologie ; Büsgen-Institut ; Abteilung Ökosystemmodellierung
- ISSN
- 1935-7524
- Language
- English