Generalized linear mixed model

In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects.^[1]^[2]^[3] They also inherit from generalized linear models the idea of extending linear mixed models to non-normal data.

Generalized linear mixed models provide a broad range of models for the analysis of grouped data, since the differences between groups can be modelled as a random effect. These models are useful in the analysis of many kinds of data, including longitudinal data.^[4]

^ Breslow, N. E.; Clayton, D. G. (1993), "Approximate Inference in Generalized Linear Mixed Models", Journal of the American Statistical Association, 88 (421): 9–25, doi:10.2307/2290687, JSTOR 2290687
^ Stroup, W.W. (2012), Generalized Linear Mixed Models, CRC Press
^ Jiang, J. (2007), Linear and Generalized Linear Mixed Models and Their Applications, Springer
^ Fitzmaurice, G. M.; Laird, N. M.; Ware, J.. (2011), Applied Longitudinal Analysis (2nd ed.), John Wiley & Sons, ISBN 978-0-471-21487-8

[1] Breslow, N. E.; Clayton, D. G. (1993), "Approximate Inference in Generalized Linear Mixed Models", Journal of the American Statistical Association, 88 (421): 9–25, doi:10.2307/2290687, JSTOR 2290687

[2] Stroup, W.W. (2012), Generalized Linear Mixed Models, CRC Press

[3] Jiang, J. (2007), Linear and Generalized Linear Mixed Models and Their Applications, Springer

[4] Fitzmaurice, G. M.; Laird, N. M.; Ware, J.. (2011), Applied Longitudinal Analysis (2nd ed.), John Wiley & Sons, ISBN 978-0-471-21487-8

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