Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K ...

In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates p exceeds the sample size n ...

A new and first of its kind book provides a practical guide for the use of modern statistical methods within agricultural and natural resources sciences. Analysis of Generalized Linear Mixed Models in ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of-fit analyses. Census tracts. Fire ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

X ij = [x ij1, ... , x ijp]' The Generalized Estimating Equation of Liang and Zeger (1986) for estimating the p ×1 vector of regression parameters is an extension of the independence estimating ...