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Activity Number: 105
Type: Topic Contributed
Date/Time: Monday, August 3, 2009 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304258
Title: Bayesian Analysis of Spatially Correlated and Repeated Ordinal Response Data with Time-Dependent Missing Covariates
Author(s): Fang Yu*+ and Ming-Hui Chen and Sudipto Banerjee and Lan Huang and Gregory J. Anderson
Companies: University of Nebraska Medical Center and University of Connecticut and The University of Minnesota and U.S. Food and Drug Administration and University of Connecticut
Address: Department of Biostatistics, Omaha, NE, 68127,
Keywords: Probit Regression ; Missing Covariates ; Markov chain Monte Carlo
Abstract:

We develop a probit regression model for spatially correlated and repeated ordinal responses and a joint model for time-dependent missing covariates using information from different sources. A new Bayesian method is developed to identify the importance of each covariate and the sensitivity of the specification of the missing covariates models is investigated. A Markov chain Monte Carlo algorithm is developed for computing the Bayesian estimates. A real plant data set is used to motivate and illustrate the proposed methodology.

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