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Activity Number: 57 - Frontiers in Bayesian Computing
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #309796
Title: Parameter-Expanded Data Augmentation for Analyzing Correlated Binary Data Using Multivariate Probit Models
Author(s): Xiao Zhang*
Companies: Michigan Technological University
Keywords: data augmentation; parameter-expanded data augmentation; correlated binary data; multivariate probit model
Abstract:

Data augmentation has been commonly utilized to analyze correlated binary data using multivariate probit models in Bayesian analysis. However, the identification issue in the multivariate probit models necessitates a rigorous Metropolis-Hastings algorithm for sampling a correlation matrix, which may cause slow convergence and inefficiency of Markov chains. It is well-known that the parameter-expanded data augmentation, by introducing a working/artificial parameter or parameter vector, makes an identifiable model be non-identifiable and improves the mixing and convergence of data augmentation components. Therefore, we develop efficient parameter-expanded data augmentations to analyze correlated binary data using multivariate probit models. We point out that the approaches, based on the non-identifiable models, circumvent a Metropolis-Hastings algorithm for sampling a correlation matrix and outperform those that entail sampling a correlation matrix. We illustrate our proposed approaches using simulation studies and through the application to a longitudinal dataset from the Six Cities study.


Authors who are presenting talks have a * after their name.

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