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Abstract Details
Activity Number:
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302
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #306467 |
Title:
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Bayesian Adaptive Calibration and Variable Selection in Linear Models with Mismeasured Covariates
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Author(s):
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Hongmei Zhang*+ and Xianzheng Huang and Jianjun Gan and Wilfried Karmaus and Tara Sabo-Attwood
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Companies:
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University of South Carolina and University of South Carolina and GlaxoSmithKline and University of South Carolina and University of Florida
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Address:
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317B, Health Sci. Bldg., Columbia, SC, 29208, United States
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Keywords:
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g-prior ;
Measurement Error ;
Prediction Loss ;
Pseudo Variables ;
Tuning Parameter
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Abstract:
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We propose a Bayesian variable selection method built upon an extended Zellner's $g$-prior in linear measurement error models. Pseudo variables are introduced into the model to facilitate choosing a tuning parameter that indirectly controls the selection of variables. Simulation results indicate that models selected using the proposed method are generally more favorable with smaller prediction losses than the models selected using classical Zellner's $g$-priors. The proposed method is further demonstrated using two data sets: gene expression data from a lung disease study and food frequency questionnaire data from a nurse study.
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Authors who are presenting talks have a * after their name.
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