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Activity Number: 76 - To Open Source, or Not
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #306916
Title: A Bayesian Approach to the Measurement –Error Problem in Regression
Author(s): Ananda Jayawardhana*
Companies: Pittsburg State University
Keywords: Measurement-Error; Bayesian simulations; Regression; Logistic Regression

In this simulation study, simple linear regression and logistic regression was studied under measurement error of the predictor variables. Measurement errors often occur in data collection. Many attempts have been made over the years to study the effect of measurement errors and their effect on parameter estimates. In this study we modeled with known predictor variables. Using R, R2OpenBUGS, and OpenBUGS we simulated data sets and added random errors and studied the parameter estimates. Overall, the introduction of small errors to predictor variables did not have a large effect on parameter estimates. Simulation results and simulation codes will be discussed in this presentation.

Authors who are presenting talks have a * after their name.

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