Advertisement

Mixed Effects Models

  • Jiming Jiang
Chapter
Part of the Springer Texts in Statistics book series (STS, volume 0)

Abstract

Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + ε, where y is a vector of observations, X is a matrix of known covariates, β is a vector of unknown regression coefficients, and ε is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies, observations are often collected from the same individuals over time.

Keywords

Variance Component Linear Mixed Model True Model Candidate Model Asymptotic Normality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of CaliforniaDavisUSA

Personalised recommendations