Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

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, ...

This paper presents an attempt to explicate some common features of several superficially diverse techniques of data analysis and to indicate how the logic of a single abstract model is relevant to ...

We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the ...

Defines the least-squares means for the fixed-effects general linear model. The report also discusses the use of least-squares means in lieu of class or subclass arithmetic means with unbalanced ...

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 ...