A Bayesian Spatial Contamination Model

• 발행기관: 한국자료분석학회
• 저자명: Jonghyun Na Taekseon Ryu Joonmyoung Kim Hansuk Kim Manjae Kwon Yongsung Joo
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.24 No.3, 919~931쪽, 전체 13쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2022.06.30

영문 초록

In environmental research, it is often the case that to cluster observations into environmentally polluted and natural groups is an important issue. The Bayesian contamination model which adopts a multivariate mixture regression model has been developed in that it aims to cluster observations and estimate the average amount of pollution. However, because the Bayesian contamination model does not take spatial correlations between observations into consideration, a Bayesian spatial contamination model is proposed. A simulation study was conducted showing that the proposed model has an advantage over the Bayesian contamination model in terms of biases and RMSE of estimators of the logistic regression parameters. We applied the proposed model into environmental data and confirmed the improvement on the model fit. Also, the clustering was reasonably performed from the environmental perspective, which was coherent with the fact that the underground water flows from the southwest side to the northeast side. This model is expected to be utilized effectively to monitor the quality of a ground or groundwater and capture the heterogeneity in it which is suspected of environmental pollution especially when the interested site consists of areas with strong spatial dependency.

목차

1. Introduction

2. Model

3. Bayesian estimation and model comparison

4. Simulation study and Application

5. Conclusion

References

키워드

clustering mixture regression model Bayesian spatial model