MCMC for Generalized Linear Mixed Models with glmmBUGS

MCMC for Generalized Linear Mixed Models with glmmBUGS

The glmmBUGS package is a bridging tool between Generalized Linear Mixed Models (GLMMs) in R and the BUGS language. It provides a simple way of performing Bayesian inference using Markov Chain Monte Carlo (MCMC) methods, taking a model formula and data frame in R and writing a BUGS model file, data file, and initial values files. Functions are provided to reformat and summarize the BUGS results...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Generalized Linear Mixed Models

Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts). McCullagh and Nelder [32] describe these in great detail and indicate that the term ‘generalized linear model’ is due to Nelder and Wedderburn [35] who described how a collection of seemingly disparate statistical techniques coul...

Generalized Linear Mixed Models

An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models

This study reports on the performance assessment of the Asian airlines using TOPSIS – a multi-criteria decision making technique – as the cornerstone method to compute efficiency scores. Subsequently, and observing indicators frequently found in literature, TOPSIS results on efficiency levels are combined with GLMM-MCMC methods to assess the impact of contextual variables on performance. The re...