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Bayesian pattern mixture model under nonignorable nonresponse for binary data
Journal of the Korean Data & Information Science Society 2019;30:907-17
Published online July 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.4.907
© 2019 Korean Data and Information Science Society.

Sukyoung An1 · Balgobin Nandram2 · Dal Ho Kim3

13Department of Statistics, Kyungpook National University
2Department of Mathematical Sciences, Worcester Polytechnic Institute
Correspondence to: Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: dalkim@knu.ac.kr
Received June 24, 2019; Revised July 12, 2019; Accepted July 12, 2019.
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
We consider a Bayesian pattern mixture model to estimate the proportion of the nite population with missing data. The pattern mixture approach is a way to model missing data. We describe the Bayesian model considering two cases for the parameter of a prior distribution. To fit the model, we use Markov chain Monte Carlo methods. We use the Gibbs sampler with grid method to get the samples of the parameters. We use the National Crime Survey data summarized by Stasny (1991) to estimate the proportion of the finite population. When considering two cases of the parameter of a prior distribution, we saw that the inference for the parameter was not sensitive in our proposed model.
Keywords : Bayesian estimation, grid methods, latent variable, pattern mixture model.