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Generalised linear mixed model analysis via sequential Monte Carlo sampling

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Generalised linear mixed model analysis via sequential Monte Carlo sampling. / Fan, Y.; Leslie, David S.; Wand, M. P.
In: Electronic Journal of Statistics, Vol. 2, 01.01.2008, p. 916-938.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fan, Y, Leslie, DS & Wand, MP 2008, 'Generalised linear mixed model analysis via sequential Monte Carlo sampling', Electronic Journal of Statistics, vol. 2, pp. 916-938. https://doi.org/10.1214/07-EJS158

APA

Fan, Y., Leslie, D. S., & Wand, M. P. (2008). Generalised linear mixed model analysis via sequential Monte Carlo sampling. Electronic Journal of Statistics, 2, 916-938. https://doi.org/10.1214/07-EJS158

Vancouver

Fan Y, Leslie DS, Wand MP. Generalised linear mixed model analysis via sequential Monte Carlo sampling. Electronic Journal of Statistics. 2008 Jan 1;2:916-938. doi: 10.1214/07-EJS158

Author

Fan, Y. ; Leslie, David S. ; Wand, M. P. / Generalised linear mixed model analysis via sequential Monte Carlo sampling. In: Electronic Journal of Statistics. 2008 ; Vol. 2. pp. 916-938.

Bibtex

@article{cf49053a861243d7bed249d5afd1d405,
title = "Generalised linear mixed model analysis via sequential Monte Carlo sampling",
abstract = "We present a sequential Monte Carlo sampler algorithm forthe Bayesian analysis of generalised linear mixed models (GLMMs). Thesemodels support a variety of interesting regression-type analyses, but per-forming inference is often extremely difficult, even when using the Bayesianapproach combined with Markov chainMonte Carlo (MCMC). The Sequen-tialMonte Carlo sampler (SMC) is a new and generalmethod for producingsamples from posterior distributions. In this article we demonstrate use ofthe SMC method for performing inference for GLMMs. We demonstratethe effectiveness of the method on both simulated and real data, and findthat sequential Monte Carlo is a competitive alternative to the availableMCMC techniques.",
keywords = "generalised additive models, longitudinal data analysis, nonparametric regression, Sequential Monte Carlo",
author = "Y. Fan and Leslie, {David S.} and Wand, {M. P.}",
year = "2008",
month = jan,
day = "1",
doi = "10.1214/07-EJS158",
language = "English",
volume = "2",
pages = "916--938",
journal = "Electronic Journal of Statistics",
issn = "1935-7524",
publisher = "Institute of Mathematical Statistics",

}

RIS

TY - JOUR

T1 - Generalised linear mixed model analysis via sequential Monte Carlo sampling

AU - Fan, Y.

AU - Leslie, David S.

AU - Wand, M. P.

PY - 2008/1/1

Y1 - 2008/1/1

N2 - We present a sequential Monte Carlo sampler algorithm forthe Bayesian analysis of generalised linear mixed models (GLMMs). Thesemodels support a variety of interesting regression-type analyses, but per-forming inference is often extremely difficult, even when using the Bayesianapproach combined with Markov chainMonte Carlo (MCMC). The Sequen-tialMonte Carlo sampler (SMC) is a new and generalmethod for producingsamples from posterior distributions. In this article we demonstrate use ofthe SMC method for performing inference for GLMMs. We demonstratethe effectiveness of the method on both simulated and real data, and findthat sequential Monte Carlo is a competitive alternative to the availableMCMC techniques.

AB - We present a sequential Monte Carlo sampler algorithm forthe Bayesian analysis of generalised linear mixed models (GLMMs). Thesemodels support a variety of interesting regression-type analyses, but per-forming inference is often extremely difficult, even when using the Bayesianapproach combined with Markov chainMonte Carlo (MCMC). The Sequen-tialMonte Carlo sampler (SMC) is a new and generalmethod for producingsamples from posterior distributions. In this article we demonstrate use ofthe SMC method for performing inference for GLMMs. We demonstratethe effectiveness of the method on both simulated and real data, and findthat sequential Monte Carlo is a competitive alternative to the availableMCMC techniques.

KW - generalised additive models

KW - longitudinal data analysis

KW - nonparametric regression

KW - Sequential Monte Carlo

U2 - 10.1214/07-EJS158

DO - 10.1214/07-EJS158

M3 - Journal article

VL - 2

SP - 916

EP - 938

JO - Electronic Journal of Statistics

JF - Electronic Journal of Statistics

SN - 1935-7524

ER -