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
Reduction in antibiotic use among US children, 1996-2000.
Jonathan A. Finkelstein,Jonathan A. Finkelstein,Christopher J. Stille,James D. Nordin,Robert L. Davis,Marsha A. Raebel,Douglas W. Roblin,Alan S. Go,David H. Smith,Christine Cole Johnson,Ken Kleinman,K. Arnold Chan,K. Arnold Chan,Richard Platt,Richard Platt +14 more
TL;DR: Attention by public health and professional organizations and the news media to antibiotic resistance may have contributed to changes in diagnostic thresholds, resulting in more judicious prescribing.
Book ChapterDOI
General Quantitative Genetic Methods for Comparative Biology
TL;DR: It is demonstrated how the phylogenetic generalised linear mixed model (PGLMM) can be a useful extension of PGLS, hence a useful tool for the comparative biologist and how the PGLMM can tackle issues such as intraspecific variance inference, phylogenetic meta-analysis, and non-Gaussian traits analysis.
Journal ArticleDOI
Spatial variation in disease resistance: from molecules to metapopulations.
TL;DR: Current literature on natural plant–pathogen associations is reviewed to determine how diversity in disease resistance is distributed at different hierarchical levels – within host individuals, within host populations, amongst host populations at the metapopulation scale and at larger regional scales.
Book ChapterDOI
An Introduction to Model-Based Geostatistics
TL;DR: The scientific focus is to study a spatial phenomenon, s(x)say, which exists throughout a continuous spatial region A ⊂ ℝ2 and can be treated as if it were a realisation of a stochastic process S(·) = {S(x): x ∈ A}.
Journal Article
Robust Likelihood-based Analysis of Multivariate Data with Missing Values
TL;DR: Simulation comparisons with other methods suggest that the model-based approach to inference from multivariate data with missing values works well in a wide range of populations, with little loss of efficiency relative to parametric models when the latter are correct.
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.