A Bayesian Approach to Estimate a Linear Regression Model with Aggregate Data

Authors

  • Aymen Rawashdeh, Dr. Yarmouk University
  • Mohammed Obeidat, Dr. Yarmouk University

DOI:

https://doi.org/10.17713/ajs.v48i4.851

Abstract

The main purpose of this paper is to perform linear regression analysis on a continuous aggregate outcome from a Bayesian perspective using a Markov chain Monte Carlo algorithm (Gibbs sampling). In many situations, data are partially available due to privacy and confidentiality of the subjects in the sample. So, in this study, the vector of outcomes, Y, is realistically assumed to be missing and is partially available through summary statistics, sum(Y), aggregated over groups of subjects, while the covariate values, X, are available
for all subjects in the sample. The results of the simulation study highlight both the efficiency of the regression parameter estimates and the predictive power of the proposed model compared with classical
methods. The proposed approach is fully implemented in an example regarding systolic blood pressure for illustrative purposes.

Author Biography

Aymen Rawashdeh, Dr., Yarmouk University

Assistant Professor of Statistics,

Department of Statistics

Yarmouk University

Irbid, Jordan

Published

2019-07-25

How to Cite

Rawashdeh, A., & Obeidat, M. (2019). A Bayesian Approach to Estimate a Linear Regression Model with Aggregate Data. Austrian Journal of Statistics, 48(4), 90-100. https://doi.org/10.17713/ajs.v48i4.851