Electronic Journal of Statistics

Generalised linear mixed model analysis via sequential Monte Carlo sampling

Y. Fan, D.S. Leslie, and M.P. Wand

Full-text: Open access

Abstract

We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chain Monte Carlo (MCMC). The Sequential Monte Carlo sampler (SMC) is a new and general method for producing samples from posterior distributions. In this article we demonstrate use of the SMC method for performing inference for GLMMs. We demonstrate the effectiveness of the method on both simulated and real data, and find that sequential Monte Carlo is a competitive alternative to the available MCMC techniques.

Article information

Source
Electron. J. Statist. Volume 2 (2008), 916-938.

Dates
First available in Project Euclid: 6 October 2008

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1223304591

Digital Object Identifier
doi:10.1214/07-EJS158

Mathematical Reviews number (MathSciNet)
MR2447345

Zentralblatt MATH identifier
1320.62178

Keywords
generalised additive models longitudinal data analysis nonparametric regression sequential Monte Carlo sampler

Citation

Fan, Y.; Leslie, D.S.; Wand, M.P. Generalised linear mixed model analysis via sequential Monte Carlo sampling. Electron. J. Statist. 2 (2008), 916--938. doi:10.1214/07-EJS158. https://projecteuclid.org/euclid.ejs/1223304591


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