Journal ArticleDOI
Approximate inference in generalized linear mixed models
Norman E. Breslow,D. G. Clayton +1 more
TLDR
In this paper, generalized linear mixed models (GLMM) are used to estimate the marginal quasi-likelihood for the mean parameters and the conditional variance for the variances, and the dispersion matrix is specified in terms of a rank deficient inverse covariance matrix.Abstract:
Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior weights and a scale factor. The random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components. For problems involving time series, spatial aggregation and smoothing, the dispersion may be specified in terms of a rank deficient inverse covariance matrix. Approximation of the marginal quasi-likelihood using Laplace's method leads eventually to estimating equations based on penalized quasilikelihood or PQL for the mean parameters and pseudo-likelihood for the variances. Im...read more
Citations
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Journal ArticleDOI
Daily patterns of locomotion expressed by american lobsters (homarus americanus) in their natural habitat
TL;DR: While there was a general tendency for lobsters in this study to be more active at night, certain factors in their natural habitat modulated this nocturnal bias, which led to a tremendous amount of variability in their daily patterns of behavior.
Journal ArticleDOI
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
TL;DR: In this article, the spatial, temporal, and spatio-temporal interaction random effects are reparameterized using the spectral decomposition of their precision matrices to establish the appropriate identifiability constraints.
Journal ArticleDOI
An Introduction to Generalized Linear Mixed Models
TL;DR: The generalized linear mixed model (GLMM) as discussed by the authors generalizes the standard linear model in three ways: accommodation of non-normally distributed responses, specification of a possibly non-linear link between the mean of the response and the predictors, and allowance for some forms of correlation in the data.
Journal ArticleDOI
Effects of Primary Care Team Social Networks on Quality of Care and Costs for Patients With Cardiovascular Disease
Marlon P. Mundt,Valerie J. Gilchrist,Michael F. Fleming,Larissa I. Zakletskaia,Wen Jan Tuan,John W. Beasley +5 more
TL;DR: Primary care teams that are more interconnected and less centralized and that have a shared team vision are better positioned to deliver high-quality cardiovascular disease care at a lower cost.
Journal ArticleDOI
Statistical models appropriate for designs often used in group-randomized trials.
TL;DR: This paper presents the adaptations of the mixed-model analysis of covariance and random coefficients models that are required for the four combinations that result from the categorization scheme used in group-randomized trials.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.