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Activity Number: 302
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306467
Title: Bayesian Adaptive Calibration and Variable Selection in Linear Models with Mismeasured Covariates
Author(s): Hongmei Zhang*+ and Xianzheng Huang and Jianjun Gan and Wilfried Karmaus and Tara Sabo-Attwood
Companies: University of South Carolina and University of South Carolina and GlaxoSmithKline and University of South Carolina and University of Florida
Address: 317B, Health Sci. Bldg., Columbia, SC, 29208, United States
Keywords: g-prior ; Measurement Error ; Prediction Loss ; Pseudo Variables ; Tuning Parameter

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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