Results 1  10
of
13
MCMC Methods for Multiresponse Generalized Linear Mixed Models: The MCMCglmm R Package
"... Generalized linear mixed models provide a flexible framework for modeling a range of data, although with nonGaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distribution ..."
Abstract

Cited by 83 (0 self)
 Add to MetaCart
(Show Context)
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with nonGaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm, implements such an algorithm for a range of model fitting problems. More than one response variable can be analysed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zeroinflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in metaanalysis. All simulation is done in C / C++ using the CSparse library for sparse linear systems. If you use the software please cite this article, as published in the Journal of Statistic Software
Splines, knots, and penalties
, 2004
"... Abstract Penalized splines have gained much popularity as a flexible tool for smoothing and semiparametric models. Two approaches have been advocated: 1) use a Bspline basis, equallyspaced knots and difference penalties ..."
Abstract

Cited by 30 (1 self)
 Add to MetaCart
(Show Context)
Abstract Penalized splines have gained much popularity as a flexible tool for smoothing and semiparametric models. Two approaches have been advocated: 1) use a Bspline basis, equallyspaced knots and difference penalties
On semiparametric regression with O’Sullivan penalized splines
 Australian and New Zealand Journal of Statistics
, 2008
"... An exposition on the use of O’Sullivan penalized splines in contemporary semiparametric regression, including mixed model and Bayesian formulations, is presented. O’Sullivan penalized splines are similar to Psplines, but have the advantage of being a direct generalization of smoothing splines. Ex ..."
Abstract

Cited by 24 (7 self)
 Add to MetaCart
An exposition on the use of O’Sullivan penalized splines in contemporary semiparametric regression, including mixed model and Bayesian formulations, is presented. O’Sullivan penalized splines are similar to Psplines, but have the advantage of being a direct generalization of smoothing splines. Exact expressions for the O’Sullivan penalty matrix are obtained. Comparisons between the two types of splines reveal that O’Sullivan penalized splines more closely mimic the natural boundary behaviour of smoothing splines. Implementation in modern computing environments such as MATLAB, R and BUGS is discussed.
Semiparametric Regression During 2003–2007
, 2008
"... Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a ..."
Abstract

Cited by 17 (5 self)
 Add to MetaCart
Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Fixed and random effects selection in linear and logistic models
 Biometrics
, 2007
"... Summary. We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact modelaveraged po ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
(Show Context)
Summary. We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact modelaveraged posterior distribution. This approach automatically identifies subsets of predictors having nonzero fixed effect coefficients or nonzero random effects variance, while allowing uncertainty in the model selection process. Default priors are proposed for the variance components and an efficient parameter expansion Gibbs sampler is developed for posterior computation. The approach is illustrated using simulated data and an epidemiologic example. Key words: Bayesian model selection; Logistic regression; Mixed effects model; Model averaging; Parameter expansion; Random effects; Variance components test; Variable selection. Introduction Logistic mixed models are widely used, flexible models for unbalanced repeated measures data. Often in linear and nonlinear mixed effects models, random effects are chosen to control for specific factors which are expected to cause random variation in the coefficients, such as batch effects and withinsubject variation in repeated measurements. A difficult question is how to decide which predictors have coefficients that vary among subjects. Standard model selection criteria and test procedures are not appropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. This problem has motivated a growing literature on frequentist tests for homogeneity of variance components. In the setting of linear mixed models with one variance component,
Actuarial statistics with generalized linear mixed models
 Insurance: Mathematics and Economics, 2006a
"... Over the last decade the use of generalized linear models (GLMs) in actuarial statistics received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh & Nelder (1989). Traditional GLMs however model a sample of independent random variables. Since actu ..."
Abstract

Cited by 14 (3 self)
 Add to MetaCart
Over the last decade the use of generalized linear models (GLMs) in actuarial statistics received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh & Nelder (1989). Traditional GLMs however model a sample of independent random variables. Since actuaries very often have repeated measurements or longitudinal data (i.e. repeated measurements over time) at their disposal, this article considers statistical techniques to model such data within the framework of GLMs. Use is made of generalized linear mixed models (GLMMs) which model a transformation of the mean as a linear function of both fixed and random effects. The likelihood and Bayesian approaches to GLMMs are explained. The models are illustrated by considering classical credibility models and more general regression models for nonlife ratemaking in the context of GLMMs. Details on computation and implementation (in SAS and WinBugs) are provided.
Mean field variational Bayesian inference for support vector machine classification
 Computational Statistics and Data Analysis
, 2014
"... be inserted by the editor) ..."
(Show Context)
A Clipped LatentVariable Model for Spatially Correlated Ordered Categorical Data
"... We propose a model for a pointreferenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially correlated continuous response data are widespread, but models for spatially correlated categorical data, and especially or ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
We propose a model for a pointreferenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially correlated continuous response data are widespread, but models for spatially correlated categorical data, and especially ordered multicategory data, are less developed. Bayesian models and methodology have been proposed for the analysis of independent and clustered ordered categorical data, and also for binary and count pointreferenced spatial data. We combine and extend these methods to describe a Bayesian model for pointreferenced (as opposed to lattice) spatially correlated ordered categorical data. We include extensive simulation results and show that our model offers superior predictive performance as compared to a nonspatial cumulative probit model and a more standard generalized linear model with spatial random effects. We demonstrate the usefulness of our model using a realworld example to predict ordered categories describing stream health within the state of Maryland. Key words: Bayesian, ordinal, benthic IBI, generalized linear mixed models
Centre for Statistical and Survey Methodology
"... University of Wollongong. For further information contact the UOW ..."
and
, 2006
"... SUMMARY. We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact modelaveraged pos ..."
Abstract
 Add to MetaCart
(Show Context)
SUMMARY. We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact modelaveraged posterior distribution. This approach automatically identifies subsets of predictors having nonzero fixed effect coefficients or nonzero random effects variance, while allowing uncertainty in the model selection process. Default priors are proposed for the variance components and an efficient parameter expansion Gibbs sampler is developed for posterior computation. The approach is illustrated using simulated data and an epidemiologic example.