Abstract Details
Activity Number:
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414
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 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 #311698
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Title:
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A Bayesian Approach to Modeling Measurement Errors
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Author(s):
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Jennifer Weeding*+ and Mark Greenwood
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Companies:
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and Montana State University
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Keywords:
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Measurement Error ;
Errors-In-Variables ;
Response Measurement Error ;
Correlated Measurement Errors ;
GSIMEX ;
Bayesian Methods
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Abstract:
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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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Authors who are presenting talks have a * after their name.
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