Estimating linear mixed effects models with truncated normally distributed random effects

Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

Estimating Realized Random Effects in Mixed Models

A common analysis objective is estimation of a realized random effect. The parameter underlying such an effect is usually defined as an average response of a realized unit, such as a cluster mean, domain mean, small area mean, or subject effect. The effects are called random effects since their occurrence is the result of some (actual or assumed) random sampling process. In mixed models, random...

Random effects selection in linear mixed models.

We address the important practical problem of how to select the random effects component in a linear mixed model. A hierarchical Bayesian model is used to identify any random effect with zero variance. The proposed approach reparameterizes the mixed model so that functions of the covariance parameters of the random effects distribution are incorporated as regression coefficients on standard nor...

Random Effects in Generalized Linear Mixed Models

In this chapter, we examine the use of special forms of correlated random e ects in the generalized linear mixed model (GLMM) setting. A special feature of our GLMM is the inclusion of random residual e ects to account for lack of t due to extra variation, outliers and other unexplained sources of variation. For random e ects, we consider, in particular, the correlation structure and improper p...

Estimating Realized Random Effects in Mixed Models with Finite Populations