Maximum Likelihood Algorithms for Generalized Linear Mixed Models

Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model

Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...

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...

Monte Carlo Local Likelihood for Estimating Generalized Linear Mixed Models

We propose the Monte Carlo local likelihood (MCLL) method for estimating generalized linear mixed models (GLMMs) with crossed random e ects. MCLL initially treats model parameters as random variables, sampling them from the posterior distribution in a Bayesian model. The likelihood function is then approximated up to a constant by tting a density to the posterior samples and dividing it by the ...

A comparison of algorithms for maximum likelihood estimation of Spatial GLM models

In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...

Restricted likelihood inference for generalized linear mixed models