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Bayesian Inference for Multivariate Ordinal Data Using Parameter Expansion

Summary: [This abstract is based on the authors' abstract.] A novel method for Bayesian inference for multivariate prohibit models using Markov chain Monte Carlo techniques is proposed that uses parameter expansion to sample correlation matrices. Inference is performed using standard Gibbs sampling methods. Bayesian methods for model selection are also discussed. The motivation for the approach is a study of how women make decisions regarding taking medications to reduce the risk of breast cancer.

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  • Topics: Data Quality
  • Keywords: Multivariate analysis, Sampling plans, Parameters, Variable data, Data analysis, Markov chains, Monte Carlo methods, Bayesian methods
  • Author: Lawrence, Earl; Bingham, Derek ; Liu, Chuanhai ; Nair, Vijayan N.
  • Journal: Technometrics