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A Bayesian approach to statistical matching using the national health screening data
Journal of the Korean Data & Information Science Society 2021;32:893-904
Published online July 31, 2021;  https://doi.org/10.7465/jkdi.2021.32.4.893
© 2021 Korean Data and Information Science Society.

Sejin Bae1 · Dal Ho Kim2

12Department of Statistics, Kyungpook National University
Correspondence to: 1 Ph.D. candidate, Department of Statistics, Kyungpook National University, Daegu 41566, Korea.
2 Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: dalkim@knu.ac.kr
Received May 23, 2021; Revised June 3, 2021; Accepted June 11, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The problem of missing data that complicates the data analysis process, is an inevitable phenomenon in various studies. Statistical matching using existing data or information can solve this problem. Many studies have been conducted on statistical matching. This includes linear regression models and nonparametric methods. However, the aforementioned methods may not perform well in small sample problems. This study attempts to address this issue from a Bayesian perspective. In particular, we verify the performance of our Bayesian-based statistical matching method in small sample problems. We use the real observed data from the National Health Screening Data to compare the proposed model with other existing methods.
Keywords : Bayesian approach, data fusion, distance hot deck, linear regression model, statistical matching.