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Restricted likelihood inference for generalized linear mixed models

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Abstract

We aim to promote the use of the modified profile likelihood function for estimating the variance parameters of a GLMM in analogy to the REML criterion for linear mixed models. Our approach is based on both quasi-Monte Carlo integration and numerical quadrature, obtaining in either case simulation-free inferential results. We will illustrate our idea by applying it to regression models with binary responses or count data and independent clusters, covering also the case of two-part models. Two real data examples and three simulation studies support the use of the proposed solution as a natural extension of REML for GLMMs. An R package implementing the methodology is available online.

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Correspondence to Ruggero Bellio.

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Bellio, R., Brazzale, A.R. Restricted likelihood inference for generalized linear mixed models. Stat Comput 21, 173–183 (2011). https://doi.org/10.1007/s11222-009-9157-4

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  • DOI: https://doi.org/10.1007/s11222-009-9157-4

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