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.
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Lavergne, C., Martinez, MJ. & Trottier, C. Empirical model selection in generalized linear mixed effects models. Computational Statistics 23, 99–109 (2008). https://doi.org/10.1007/s00180-007-0071-y
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DOI: https://doi.org/10.1007/s00180-007-0071-y