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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Multilevel models for survival analysis with random effects.
TL;DR: Estimating equations for a three-level hierarchical survival model are developed in detail and such a model is applied to analyze a set of chronic granulomatous disease data on recurrent infections as an illustration with both hospital and patient effects being considered as random.
Journal ArticleDOI
Visual short-term memory in the first year of life: capacity and recency effects.
TL;DR: A span task was developed to assess the amount of information infants could hold in short-term memory, and there were modest cross-age correlations, indicating that individual differences in memory capacity showed some stability from age to age.
Journal ArticleDOI
Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses
TL;DR: In a Mendelian randomization study with a binary disease outcome the bias associated with estimating the phenotype-disease log odds ratio may be of practical importance and so estimates should be subject to a sensitivity analysis against different amounts of hypothesized confounding.
Journal ArticleDOI
Spatial parasite ecology and epidemiology: a review of methods and applications
Rachel L. Pullan,Hugh J. W. Sturrock,Ricardo J. Soares Magalhães,Archie C. A. Clements,Simon Brooker +4 more
TL;DR: This review provides an overview of the spatial statistical methods available to parasitologists, ecologists and epidemiologists and discusses how such methods have yielded new insights into the ecology and epidemiology of infection and disease.
Journal ArticleDOI
Stochastic Differential Mixed-Effects Models
TL;DR: In this paper, the authors proposed a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects in stochastic differential mixed-effects models.
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.