Empirical model selection in generalized linear mixed effects models

  • Authors:

  • Christian Lavergne;Marie-José Martinez;Catherine Trottier

  • Affiliations:

  • Institut de Mathématiques et de Modélisation de Montpellier, UMR CNRS 5149, Equipe de Probabilités et Statistique, Université Montpellier II, Montpellier Cedex 5, France 34095;Institut de Mathématiques et de Modélisation de Montpellier, UMR CNRS 5149, Equipe de Probabilités et Statistique, Université Montpellier II, Montpellier Cedex 5, France 34095;Institut de Mathématiques et de Modélisation de Montpellier, UMR CNRS 5149, Equipe de Probabilités et Statistique, Université Montpellier II, Montpellier Cedex 5, France 34095

  • Venue:
  • Computational Statistics
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper focuses on model selection in generalized linear mixed models using an information criterion approach. In these models in general, the response marginal distribution cannot be analytically derived. Thus, for parameter estimation, two approximations are revisited both leading to iterative model linearizations. We propose simple model selection criteria adapted from information criteria and based on the linearized model obtained at convergence of the algorithm. The quality of derived criteria are evaluated through simulations.