Electronic Journal of Statistics

A note on conditional Akaike information for Poisson regression with random effects

Heng Lian

Full-text: Open access

Abstract

A popular model selection approach for generalized linear mixed-effects models is the Akaike information criterion, or AIC. Among others, [7] pointed out the distinction between the marginal and conditional inference depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by [5]. We show that the similar strategy extends to Poisson regression with random effects, where conditional AIC can be obtained based on our observations. Simulation studies demonstrate the usage of the criterion.

Article information

Source
Electron. J. Statist., Volume 6 (2012), 1-9.

Dates
First available in Project Euclid: 5 January 2012

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1325772677

Digital Object Identifier
doi:10.1214/12-EJS665

Mathematical Reviews number (MathSciNet)
MR2879670

Zentralblatt MATH identifier
1334.62140

Subjects
Primary: 62J12: Generalized linear models

Keywords
Akaike information AIC model selection Poisson regression

Citation

Lian, Heng. A note on conditional Akaike information for Poisson regression with random effects. Electron. J. Statist. 6 (2012), 1--9. doi:10.1214/12-EJS665. https://projecteuclid.org/euclid.ejs/1325772677


Export citation

References

  • [1] Burnham, K. P. and Anderson, D. P. (1998)., Model selection and inference: a practical information-theoretical approach. Springer, New York.
  • [2] Donohue, M. C., Overholser, R., Xu, R. and Vaida, F. (2011). Conditional Akaike information under generalized linear and proportional hazards mixed models., Biometrika 98 685–700.
  • [3] Hodges, J. S. and Sargent, D. J. (2001). Counting degrees of freedom in hierarchical and other richly-parameterised models., Biometrika 88 367–379.
  • [4] Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models., Journal of the Royal Statistical Society Series B-Methodological 58 619–656.
  • [5] Liang, H., Wu, H. L. and Zou, G. H. (2008). A note on conditional AIC for linear mixed-effects models., Biometrika 95 773–778.
  • [6] Ruppert, D., Wand, M. P. and Carroll, R. J. (2003)., Semiparametric regression 12. Cambridge University Press.
  • [7] Vaida, F. and Blanchard, S. (2005). Conditional Akaike information for mixed-effects models., Biometrika 92 351–370.