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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The dilemma of ICD implant testing.
TL;DR: ICD implant testing is too risky in ∼5% of recipients and may not be worth the risks in 10–30%.
Journal ArticleDOI
Mapping the densities of malaria vectors within a single village
TL;DR: Bayesian techniques derived for use in cancer epidemiology are applied in order to map densities of Anopheles gambiae s.l. and A. funestus in a Tanzanian village where there is intense transmission of Plasmodium falciparum malaria.
Journal ArticleDOI
Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
TL;DR: Two simple models for binary response data were studied, and the effects of assuming normality or of using a nonparametric fitting procedure for random effects, when the true distribution is potentially far from normal.
Journal ArticleDOI
Independent effects of fragmentation on forest songbirds: an organism-based approach.
TL;DR: The results support the hypothesis that landscape pattern is important for some species only when the amount of suitable habitat is low, and indicate that manipulating landscape pattern may reduce negative impacts of habitat loss for Ovenbird, but not Blackburnian Warbler.
Journal ArticleDOI
Respiratory effects associated with indoor nitrogen dioxide exposure in children.
TL;DR: Exposure to NO2 at hourly peak levels of the order of > or = 80 ppb, compared with background levels of 20 ppb was associated with a significant increase in sore throat, colds and absences from school, and significant dose-response relationships were demonstrated.
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.