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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Changes in muscle cell cation regulation and meat quality traits are associated with genetic selection for high body weight and meat yield in broiler chickens
TL;DR: Broiler genotypes were over three times heavier, their plasma creatine kinase activity (CK), a marker of muscle tissue damage, was higher, their breast muscle colour was lighter, and their initial and final pH of their muscles were lower, the pH change was higher and their breast Muscle was more tender.
Bayesian Variable Selection for Random Intercept Modeling of Gaussian and non-Gaussian Data
TL;DR: In this article, the distribution of heterogeneity p(β1,..., βN ) is defined as a smoothing prior which ties the random intercepts together and encourages shrinkage of βi toward the overall intercept by borrowing strength from observations of other subjects.
Journal ArticleDOI
Estimating precision, repeatability, and reproducibility from Gaussian and non- Gaussian data: a mixed models approach
TL;DR: This paper generalizes precision, repeatability, reproducibility, and intermediate precision by placing them within the linear mixed model framework, which is extended to the generalizedlinear mixed model setting, so that both Gaussian as well as non-Gaussian data can be employed.
Journal ArticleDOI
Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae
Daphne H. E. W. Huberts,Javier González,Sung Sik Lee,Athanasios Litsios,Georg Hubmann,Ernst Wit,Matthias Heinemann +6 more
TL;DR: A meta-analysis of replicative lifespan (RLS) data revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment, and it was found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies.
Journal ArticleDOI
Multivariate covariance generalized linear models
Wagner Hugo Bonat,Bent Jørgensen +1 more
TL;DR: In this article, a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models is proposed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices.
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.