Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 9, Number 1 (2015), 135-152.
A sequential reduction method for inference in generalized linear mixed models
The likelihood for the parameters of a generalized linear mixed model involves an integral which may be of very high dimension. Because of this intractability, many approximations to the likelihood have been proposed, but all can fail when the model is sparse, in that there is only a small amount of information available on each random effect. The sequential reduction method described in this paper exploits the dependence structure of the posterior distribution of the random effects to reduce substantially the cost of finding an accurate approximation to the likelihood in models with sparse structure.
Electron. J. Statist., Volume 9, Number 1 (2015), 135-152.
Received: August 2014
First available in Project Euclid: 6 February 2015
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Ogden, Helen E. A sequential reduction method for inference in generalized linear mixed models. Electron. J. Statist. 9 (2015), no. 1, 135--152. doi:10.1214/15-EJS991. https://projecteuclid.org/euclid.ejs/1423229753
- R code for sequential reduction. The R package glmmsr for sequential reduction, and code to reproduce the examples.