Previous Chapter Chapter 39: Generalized Linear Mixed Models Next Chapter

  • Citations
  • Add to My List
  • Text Size

Generalized Linear Mixed Models
Generalized linear mixed models
Introduction

The generalized linear mixed model (GLMM) is an extension and a combination of both the generalized linear model (GLM) and the linear mixed model (LMM). GLMs, which were proposed by Nelder and Wedderburn (1972) and were later carefully discussed in McCullagh and Searle (2001), contain an extensive family of models and techniques helpful in data analysis with non-normal errors and constitute one of the most important statistical tools used today. Like ordinary linear models, the observations in GLMs are assumed to be independent. However, the structure of the data can sometimes seriously violate this assumption. For example, in a longitudinal study, responses on a specific subject were usually measured repeatedly during several occasions. Likewise, in the clustered or ...

Looks like you do not have access to this content.

Login

Don’t know how to login?

Click here for free trial login.

Back to Top

Copy and paste the following HTML into your website