摘要：Generalized linear mixed models (GLMMs) encompass large class of statistical mo d- els, with a vast range of applications areas. GLMMs extend the linear mixed models allowing for di.erent types of response variable. Three most common data types are continuous, counts and binary and standard distributions for these types of response variables are Gaussian, Poisson and binomial, respectively. Despite that .exibility, there are situations where the response variable is continuous, but bounded, such as rates, p ercentages, indexes and proportions. In such situations the usual GLMMs are not ad- equate b ecause bounds are ignored and the beta distribution can be used. Likelihood and Bayesian inference for beta mixed models are not straightforward demanding a computational overhead. Recently, a new algorithm for Bayesian inference called INLA (Integrated Nested Laplace Approximation) was proposed. INLA allows computation of many Bayesian GLMMs in a reasonable amount time allowing extensive comparison among models. We explore Bayesian inference for beta mixed models by INLA. We dis- cuss the choice of prior distributions, sensitivity analysis and mo del selection measures through a real data set. The results obtained from INLA are compared with those ob- tained by an MCMC algorithm and likelihoo d analysis. We analyze data from an study on a life quality index of industry workers collected according to a hierarchical sampling scheme. Results show that the INLA approach is suitable and faster to fit the proposed b eta mixed models producing results similar to alternative algorithms and with easier handling of modeling alternatives. Sensitivity analysis, measures of go odness of fit and model choice are discussed.
关键词：Hierarchical models ; Bayesian inference ; Beta law ; Integrated Laplace ; approximation ; Life quality
