A Study on Imputation for Missing Data using the Kriging

• 발행기관: 한국자료분석학회
• 저자명: 최승배(Seungbae Choi) Geneveve Parreno 김규곤(Kyu Kon Kim) 강창완(Changwan Kang)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.17 No.6, 2857~2866쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2015.12.30

영문 초록

Geostatistics is a branch of statistical theory concerned with problems of spatial serial data, interpolation and mapping of distributed data, and related problems. The missing data are produced in many fields concerned with geostatistical data. Generally, in case of continuous data, we use the average as a method for imputation of missing data. However, using an average is no longer a good method to impute for the geostatistical missing data as there exist a spatial autocorrelation among them. In this study, we suggest the ‘kriging’ that is spatial statistics method as a method to impute for those spatial missing data, and then compare it with general average method in aspect to imputation superiority. To show the usefulness of the proposed method, we perform a small simulation study based on the PRESS statistic and show an empirical example with rainfall data from the Philippines.

목차

1. Introduction

2. Outline of Spatial Statistics

3. Simulation study

4. Empirical example

5. Concluding Remarks

References

키워드

Sample variogram Ordinary kriging Spatial imputation PRESS