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 ...

A model selection criterion, called the IC Q statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu, and Tang, 2008, Journal of the American Statistical Association 103, 1648-1658).

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 ...

The consequence of having random effects in your model is that some observations are no longer uncorrelated but instead have a covariance that depends on the variance of the random effect. In fact, a ...

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 ...

Purushottam Papatla, A Multiplicative Fixed-Effects Model of Consumer Choice, Marketing Science, Vol. 15, No. 3 (1996), pp. 243-261 ...

Mixed Effect Models and Hierarchical Models Mixed Effect Models and Hierarchical Models Course Topics This course will discuss what mixed models are, why they are called "mixed" models, what is a ...