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Abstract Details
Activity Number:
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26
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #300709 |
Title:
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Robust Joint Modeling of Event Time and Longitudinal Data with Skewness and Measurement Errors in Covariates
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Author(s):
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Yangxin Huang*+ and Getachew Dagne and Joan Hu
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Companies:
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University of South Florida and University of South Florida and Simon Fraser University
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Address:
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13201 Bruce B. Downs Blvd., Tampa, FL, 33612,
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Keywords:
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Bayesian analysis ;
Covariate measurement errors ;
Longitudinal data ;
Semiparametric mixed-effects joint models ;
Skew-elliptical distributions ;
Time-to-event
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Abstract:
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Normality of model random errors is a routine assumption for mixed-effects models in many longitudinal studies, but it may be unrealistically obscuring important features of subject variations. Covariates are usually introduced in the models to partially explain inter-subject variations, but some covariates may be measured with substantial errors. In many practical situations, the time-to-event of interest is likely related to the longitudinal response and covariate processes. This paper investigates a class of Bayesian semiparametric NLME joint models with skew-elliptical distributions which incorporates three components: response and covariate longitudinal processes in which model errors are skew-elliptical distributions including skew-normal and skew-t distributions, and an accelerated failure time process with an unspecified distribution that has the Dirichlet process prior. We illus
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Authors who are presenting talks have a * after their name.
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