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Activity Number:
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169
- Advanced Bayesian Topics (Part 2)
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #318318
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Title:
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Bayesian Analysis of Multivariate Incomplete Ordinal Data Using Multivariate Probit Models
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Author(s):
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Xiao Zhang*
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Companies:
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Michigan Technological University
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Keywords:
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Multivariate probit models;
multivariate ordinal data;
missing data mechanisms;
missing data;
MCMC
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Abstract:
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Multivariate probit models have been explored to analyze multivariate ordinal data. However, missing values are inevitable. We motivate to investigate Bayesian methods using the multivariate probit models for analyzing multivariate ordinal data under various missing data mechanisms. We conduct a thorough simulation study to investigate the performance of our proposed methods and compare them with the two available imputation methods - multivariate normal based and chain equation methods. For illustration, we present applications using data from the smoking cessation treatment study for low income community corrections smokers and using data from the 2019 American Community Survey (ACS) Public Use Microdata Sample (PUMS) files.
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Authors who are presenting talks have a * after their name.
