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
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Journal ArticleDOI
Highway verges as habitat providers for small mammals in agrosilvopastoral environments
Fernando Ascensão,Fernando Ascensão,Anthony P. Clevenger,Clara Grilo,Clara Grilo,J. Filipe,Margarida Santos-Reis +6 more
TL;DR: In this paper, the importance of fenced highway verges as habitat for small mammals in Mediterranean agrosilvopastoral landscapes was assessed, and the authors suggest the adoption of management practices to increase the height and cover of herbaceous and shrub layers in road verges, together with creating grazing controlled areas in highway vicinity.
Journal ArticleDOI
Hierarchical Bayes GLMs for the analysis of spatial data: An application to disease mapping
TL;DR: In this paper, a hierarchical Bayes generalized linear model approach is taken which connects the local areas, thereby enabling one to "borrow strength" to estimate cancer incidence rates for local areas.
Journal ArticleDOI
A note on the estimation of the multinomial logistic model with correlated responses in SAS
Oliver Kuss,Dale McLerran +1 more
TL;DR: The main motivation for this work are two recent papers that recommend estimating multinomial logistic models with correlated responses by using a Poisson likelihood which is statistically correct but computationally inefficient.
Journal ArticleDOI
Is there much variation in variation? Revisiting statistics of small area variation in health services research
Berta Ibáñez,Julián Librero,Enrique Bernal-Delgado,S Peiró,Beatriz González López-Valcárcel,Natalia Martínez,Felipe Aizpuru +6 more
TL;DR: Empirical Bayes (EB) statistics seems to be a good alternative to more conventional statistics used in small-area variation analysis in health service research because of its robustness and ability to discriminate between different degrees of heterogeneity.
Journal ArticleDOI
Chemoprophylactic effects of azithromycin against Rhodococcus equi–induced pneumonia among foals at equine breeding farms with endemic infections
TL;DR: Azithromycin chemoprophylaxis effectively reduced the cumulative incidence of pneumonia attributable to R equi among foals at breeding farms with endemic R equo infections and there was no evidence of resistance to azithromyzin.
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.