Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection

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...

Maximum Likelihood for Generalized Linear Model and Generalized Estimating Equations

The most frequent methods to analyze statistical data are the regression methods, whereby the maximum likelihood method or that of least squares. On the one hand, in the context of the longitudinal data or repeated measurements, these data are often unbalanced; on the other hand, their law of probability are not normal rending the passage to other models with other methods of estimate possible....

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 ...

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