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Abstract Details
Activity Number:
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508
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #300663 |
Title:
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Bayesian Inference for Nonlinear Mixed-Effects Tobit Models with Skew-Elliptical Distributions
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Author(s):
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Getachew Dagne*+
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Companies:
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University of South Florida
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Address:
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13201 Bruce B. Downs Blvd., MDC 56 , Tampa, FL, 33612, U.S.
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Keywords:
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Tobit model ;
Mixed-effects models ;
censored data ;
Bayesian inference ;
HIV/AIDS
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Abstract:
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Censored data are characteristics of many bioassays in HIV/AIDS studies where assays may not be sensitive enough to determine gradations in viral load determination among those below a detectable threshold. Not accounting for such left-censoring appropriately can lead to biased parameter estimates in most data analysis. To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew-elliptical distributions. Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable. A Bayesian modeling approach via Markov Chain Monte Carlo (MCMC) algorithm is used to estimate model parameters. The proposed methods are illustrated using real data from an HIV/AIDS study.
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Authors who are presenting talks have a * after their name.
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