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
More filters
Journal ArticleDOI
Sensitivity Analysis for Bayesian Hierarchical Models
TL;DR: This work proposes a novel formal approach to prior sensitivity analysis which quantifies sensitivity without the need for a model re-run, and develops a ready-to-use priorSens package in R which can be used to detect high prior sensitivities of some parameters as well as identifiability issues in possibly over-parametrized Bayesian hierarchical models.
Journal ArticleDOI
Multilevel Analysis with Few Clusters: Improving Likelihood-based Methods to Provide Unbiased Estimates and Accurate Inference
TL;DR: In this paper, the authors show that linear multilevel models are unbiased in linear multi-level models and demonstrate how inferential problems can be overcome by using restricted maximum-likelihood estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference.
Journal ArticleDOI
Predicting Severe Infection and Effects of Hypogammaglobulinemia During Therapy With Rituximab in Rheumatic and Musculoskeletal Diseases
Yuzaiful Md Yusof,Edward M Vital,Damien McElvenny,Elizabeth M A Hensor,S. Das,Shouvik Dass,Andy C. Rawstron,Maya H Buch,Paul Emery,Sinisa Savic +9 more
TL;DR: To evaluate predictors of serious infection events during rituximab (RTX) therapy and effects of hypogammaglobulinemia on SIE rates, and humoral response and its persistence after discontinuation of RTX in the treatment of rheumatic and musculoskeletal diseases (RMDs).
Book ChapterDOI
Bayesian Data—Model Integration in Plant Physiological and Ecosystem Ecology
Kiona Ogle,Jarrett J. Barber +1 more
Journal ArticleDOI
Evaluating the effectiveness of a participatory ergonomics approach in reducing the risk and severity of injuries from manual handling
TL;DR: The success of the intervention supports the adoption of a participatory ergonomics approach in reducing the rate and consequence of injuries in the workplace.
References
More filters
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.