Identifying causal relationships from observational data is not easy. Still, researchers are often interested in examining the effects of policy changes.

In many applications of generalized linear mixed models (GLMMs), there is a hierarchical structure in the effects that needs to be taken into account when performing variable selection. A prime ...

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 MIXED procedure provides easy accessibility to a variety of mixed models useful in many common statistical analyses, including split-plot designs, repeated measures, random coefficients, best ...

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 ...

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 ...

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 ...

In analysis of variance (ANOVA), a popular statistical technique, and several other methodologies, there are two types of factor models: fixed effects and random effects.