Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models

DOI10.1016/j.csda.2015.12.010zbMath1468.62150OpenAlexW2230281058MaRDI QIDQ1659251

K. O. Evans, C. S. Oedekoven, M. L. Mackenzie, Ruth King, L. W. jun. Burger, Stephen T. Buckland

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.12.010

zbMATH Keywords

random effectsreparameterizationpoint transect sampling``Metropolis-Hastingscombined likelihood

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12) Monte Carlo methods (65C05)

Related Items (1)

Bayesian hierarchical modelling of continuous non‐negative longitudinal data with a spike at zero: An application to a study of birds visiting gardens in winter

Uses Software

• glmmAK

Cites Work

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• A general framework for the parametrization of hierarchical models
• Bayesian methods for hierarchical distance sampling models
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• Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution
• Improving the acceptance rate of reversible jump MCMC proposals
• Delayed rejection in reversible jump Metropolis-Hastings
• A novel reversible jump algorithm for generalized linear models
• Statistical Models
• Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions
• Efficient parametrisations for normal linear mixed models
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications

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