Likelihood inference in generalized linear mixed models with two components of dispersion using data cloning

Mahmoud Torabi

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4259-4265

Abstract: This paper studies generalized linear mixed models (GLMMs) with two components of dispersion. The frequentist analysis of linear mixed model (LMM), and particularly of GLMM, is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of LMM and GLMM computationally convenient. The recent introduction of the method of data cloning has made frequentist analysis of mixed models also equally computationally convenient. We use data cloning to conduct frequentist analysis of GLMMs with two components of dispersion based on maximum likelihood estimation (MLE). The resultant estimators of the model parameters are efficient. We discuss the performance of the MLE using the well known salamander mating data, and also through simulation studies.

Keywords: Bayesian computation; Efficiency; Hierarchical models; Random effects; Variance components (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4259-4265

DOI: 10.1016/j.csda.2012.04.008

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