Fixed and Random Effects 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 ...

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 ...

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?

We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is ...

Comparing random-effects and fixed-effects modelling approaches with the Preece–Baines (PB) model reveals that, for simulated data, the Bayesian approach towards model comparison is effective in ...