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
Changes in preferences for life-sustaining treatment among older persons with advanced illness
TL;DR: SDiversity among older persons with advanced illness regarding treatment preferences persists over time, and there is a decreased willingness to undergo highly burdensome therapy or to risk severe disability in order to avoid death over time and with declining health status.
Journal ArticleDOI
Statistical inference in generalized linear mixed models: a review.
TL;DR: A review of statistical inference in generalized linear mixed models (GLMMs) and an overview of available methods for testing hypotheses about the parameters of GLMMs, which are suitable for the analysis of non-normal data with a clustered structure.
Journal ArticleDOI
Bayesian spatial and ecological models for small-area accident and injury analysis
TL;DR: A large regional variation in MVAI in males aged 0-24 in British Columbia, Canada, in 1990-1999 is indicated, and that adjusting for appropriate risk factors eliminates nearly all the variation observed.
Journal ArticleDOI
A Smooth Nonparametric Estimate of a Mixing Distribution Using Mixtures of Gaussians
TL;DR: In this article, the authors proposed a method of estimating mixing distributions using maximum likelihood over the class of arbitrary mixtures of Gaussians subject to the constraint that the component variances be greater than or equal to some minimum value h.
Journal ArticleDOI
Effects of event knowledge in processing verbal arguments
TL;DR: In self-paced reading and event-related brain potential (ERP) experiments, comprehenders dynamically combine information about real-world events based on intrasentential agents and verbs, and this combination then rapidly influences online sentence interpretation.
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.