Electronic Journal of Statistics

A sequential reduction method for inference in generalized linear mixed models

Helen E. Ogden

Full-text: Open access

Abstract

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.

Article information

Source
Electron. J. Statist., Volume 9, Number 1 (2015), 135-152.

Dates
Received: August 2014
First available in Project Euclid: 6 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1423229753

Digital Object Identifier
doi:10.1214/15-EJS991

Mathematical Reviews number (MathSciNet)
MR3306573

Zentralblatt MATH identifier
1307.62057

Subjects
Primary: 62F10: Point estimation
Secondary: 62J15: Paired and multiple comparisons

Keywords
Graphical model intractable likelihood Laplace approximation pairwise comparison sparse grid interpolation

Citation

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


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