Quasi-Monte Carlo estimation in generalized linear mixed models

A - Papers appearing in refereed journals

Pan, J. and Thompson, R. 2007. Quasi-Monte Carlo estimation in generalized linear mixed models. Computational Statistics & Data Analysis. 51 (12), pp. 5765-5775.

AuthorsPan, J. and Thompson, R.
Abstract

Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, but parameter estimation is very challenging because the likelihood may involve high-dimensional integrals that are analytically intractable. Gauss–Hermite quadrature (GHQ) approximation can be applied but is only suitable for low-dimensional random effects. Based on the Quasi-Monte Carlo (QMC) approximation, a heuristic approach is proposed to calculate the maximum likelihood estimates of parameters in the GLMM. The QMC points scattered uniformly on the high-dimensional integration domain are generated to replace the GHQ nodes. Compared to the GHQ approximation, the proposed method has many advantages such as its affordable computation, good approximation and fast convergence. Comparisons to the penalized quasi-likelihood estimation and Gibbs sampling are made using a real dataset and a simulation study. The real dataset is the salamander mating dataset whose modelling involves six 20-dimensional intractable integrals in the likelihood.

KeywordsGeneralized linear mixed models; High-dimensional integrals; Maximum likelihood estimates; Quasi-Monte Carlo approximation; Salamander data analysis
Year of Publication2007
JournalComputational Statistics & Data Analysis
Journal citation51 (12), pp. 5765-5775
Digital Object Identifier (DOI)doi:10.1016/j.csda.2006.10.003
Open accessPublished as non-open access
Funder project or codeCentre for Mathematical and Computational Biology (MCB)
Research in statistics relevant to biological processes
Output statusPublished
Publication dates
Online10 Nov 2006
Publication process dates
Accepted05 Oct 2006
Copyright licensePublisher copyright
ISSN0167-9473
PublisherElsevier Science Bv

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