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Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models

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Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. / Christensen, O. F.; Møller, J.; Waagepetersen, R. P.
In: Methodology and Computing in Applied Probability, Vol. 3, No. 3, 09.2001, p. 309-327.

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

Harvard

Christensen, OF, Møller, J & Waagepetersen, RP 2001, 'Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models', Methodology and Computing in Applied Probability, vol. 3, no. 3, pp. 309-327. https://doi.org/10.1023/A:1013779208892

APA

Christensen, O. F., Møller, J., & Waagepetersen, R. P. (2001). Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. Methodology and Computing in Applied Probability, 3(3), 309-327. https://doi.org/10.1023/A:1013779208892

Vancouver

Christensen OF, Møller J, Waagepetersen RP. Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. Methodology and Computing in Applied Probability. 2001 Sept;3(3):309-327. doi: 10.1023/A:1013779208892

Author

Christensen, O. F. ; Møller, J. ; Waagepetersen, R. P. / Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. In: Methodology and Computing in Applied Probability. 2001 ; Vol. 3, No. 3. pp. 309-327.

Bibtex

@article{681596d679ad4ac29862c6fcddccd7f3,
title = "Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models",
abstract = "Conditional simulation is useful in connection with inference and prediction for a generalized linear mixed model. We consider random walk Metropolis and Langevin-Hastings algorithms for simulating the random effects given the observed data, when the joint distribution of the unobserved random effects is multivariate Gaussian. In particular we study the desirable property of geometric ergodicity, which ensures the validity of central limit theorems for Monte Carlo estimates.",
keywords = "conditional simulation - generalized linear mixed model - geometric ergodicity - Langevin-Hastings algorithm - Markov chain Monte Carlo - random walk Metropolis algorithm",
author = "Christensen, {O. F.} and J. M{\o}ller and Waagepetersen, {R. P.}",
year = "2001",
month = sep,
doi = "10.1023/A:1013779208892",
language = "English",
volume = "3",
pages = "309--327",
journal = "Methodology and Computing in Applied Probability",
issn = "1387-5841",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models

AU - Christensen, O. F.

AU - Møller, J.

AU - Waagepetersen, R. P.

PY - 2001/9

Y1 - 2001/9

N2 - Conditional simulation is useful in connection with inference and prediction for a generalized linear mixed model. We consider random walk Metropolis and Langevin-Hastings algorithms for simulating the random effects given the observed data, when the joint distribution of the unobserved random effects is multivariate Gaussian. In particular we study the desirable property of geometric ergodicity, which ensures the validity of central limit theorems for Monte Carlo estimates.

AB - Conditional simulation is useful in connection with inference and prediction for a generalized linear mixed model. We consider random walk Metropolis and Langevin-Hastings algorithms for simulating the random effects given the observed data, when the joint distribution of the unobserved random effects is multivariate Gaussian. In particular we study the desirable property of geometric ergodicity, which ensures the validity of central limit theorems for Monte Carlo estimates.

KW - conditional simulation - generalized linear mixed model - geometric ergodicity - Langevin-Hastings algorithm - Markov chain Monte Carlo - random walk Metropolis algorithm

U2 - 10.1023/A:1013779208892

DO - 10.1023/A:1013779208892

M3 - Journal article

VL - 3

SP - 309

EP - 327

JO - Methodology and Computing in Applied Probability

JF - Methodology and Computing in Applied Probability

SN - 1387-5841

IS - 3

ER -