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A modified conditional Metropolis–Hastings sampler

A modified conditional Metropolis–Hastings sampler for general state spaces is introduced. Under specified conditions, this modification can lead to substantial gains in statistical efficiency while maintaining the overall quality of convergence. Results are illustrated in two settings: a toy bivariate Normal model and a Bayesian version of the random effects model.
Year of publication:
2014
Authors: Johnson, Alicia A. ; Flegal, James M.
Published in:
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 78.2014, C, p. 141-152
Publisher: Elsevier
Subject: Markov chain Monte Carlo | Metropolis–Hastings | Gibbs sampler | Geometric ergodicity
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Type of publication: Article
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Persistent link: https://www.econbiz.de/10010785343
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