Parameter expansion for sampling a correlation matrix: an efficient GPX-RPMH algorithm

DOI10.1080/00949650701519635zbMath1431.62019OpenAlexW2126200513MaRDI QIDQ3615028

Xue Feng Liu

Publication date: 17 March 2009

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650701519635

zbMATH Keywords

correlation matrixgeneralized candidate priorGPX-RPMH algorithmtarget prior

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15)

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Cites Work

• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Optimal scaling for various Metropolis-Hastings algorithms.
• Properties of prior and posterior distributions for multivariate categorical response data models
• Efficient estimation of covariance selection models
• Sampling-Based Approaches to Calculating Marginal Densities
• The Calculation of Posterior Distributions by Data Augmentation
• The comparison of sample covariance matrices using likelihood ratio tests
• Inference and missing data
• Analysis of multivariate probit models
• A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses
• Nonconjugate Bayesian Estimation of Covariance Matrices and Its Use in Hierarchical Models
• Regeneration in Markov Chain Samplers
• Bayesian correlation estimation

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