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Activity Number: 414
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #311698
Title: A Bayesian Approach to Modeling Measurement Errors
Author(s): Jennifer Weeding*+ and Mark Greenwood
Companies: and Montana State University
Keywords: Measurement Error ; Errors-In-Variables ; Response Measurement Error ; Correlated Measurement Errors ; GSIMEX ; Bayesian Methods
Abstract:

The impacts of ignoring covariate measurement error in regression models have been well documented and include bias in parameter estimates and a loss of power. Measurement error in the response variable has received less attention and correlated measurement errors (between the response and explanatory variables) even less. A Bayesian model that accounts for measurement errors is implemented in the simple linear regression setting, with extensions to the correlated measurement error setting and multiple linear regression setting. A simulation study is used to explore this approach, and allows for comparison to other popular measurement error correction methods (SIMEX, method of moments, ordinary least squares-no correction). The Bayesian measurement error model provided approximately unbiased results in all cases considered and directly provides a corrected estimate of unexplainable random variation.


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