Generalized linear mixed models: a review and some extensions. (English) Zbl 1331.62361
Summary: [N. E. Breslow and D. G. Clayton, J. Am. Stat. Assoc. 88, No. 421, 9–25 (1993; Zbl 0775.62195)] was, and still is, a highly influential paper mobilizing the use of generalized linear mixed models in epidemiology and a wide variety of fields. An important aspect is the feasibility in implementation through the ready availability of related software in SAS (SAS Institute, PROC GLIMMIX, SAS Institute Inc., http://www.sas.com, 2007), S-plus (Insightful Corporation, S-PLUS 8, Insightful Corporation, Seattle, WA, http://www.insightful.com, 2007), and R (R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org, 2006) for example, facilitating its broad usage. This paper reviews background to generalized linear mixed models and the inferential techniques which have been developed for them. To provide the reader with a flavor of the utility and wide applicability of this fundamental methodology we consider a few extensions including additive models, models for zero-heavy data, and models accommodating latent clusters.
MSC:
| 62J12 | Generalized linear models (logistic models) |
| 62F10 | Point estimation |
| 62J99 | Linear inference, regression |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62-02 | Research exposition (monographs, survey articles) pertaining to statistics |
Keywords:
generalized linear mixed model; random effects; longitudinal data analysis; penalized quasi-likelihoodCitations:
Zbl 0775.62195References:
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