Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Approximate Bayesian inference in spatial GLMM with skew normal latent variables
Computational Statistics & Data Analysis
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The data cloning method is a new computational tool for computing maximum likelihood estimates in complex statistical models such as mixed models. This method is synthesized with integrated nested Laplace approximation to compute maximum likelihood estimates efficiently via a fast implementation in generalized linear mixed models. Asymptotic behavior of the hybrid data cloning method is discussed. The performance of the proposed method is illustrated through a simulation study and real examples. It is shown that the proposed method performs well and rightly justifies the theory. Supplemental materials for this article are available online.