Central to the idea of variance components models is the idea of fixed and random effects. Each effect in a variance components model must be classified as either a fixed or a random effect. Fixed ...

It is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed-effects (LME) model. Typical ...

In many experimental situations, a response surface design is divided into several blocks to control an extraneous source of variation. The traditional approach in most response surface applications ...

The main focus of this course will be on linear mixed models. That is, linear models with fixed effects and random effects. Some topics we’ll discuss are: When would I want to use a random effect? How ...

This technical note discusses fixed effects models. Though a unified example, the note shows how omitted variable bias can plague estimates in cross-section regressions and how focusing attention on ...

Ask yourself the following questions. If you need some background, look up material on "mixed effects","hierarchical models" in wikipedia. What is a linear model? What is an additive effects model?

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 ...