응용통계연구 (The Korean Journal of Applied Statistics)

  • 제27권7호
  • /
  • Pages.1229-1241
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  • 2014
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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이상점 영향력 축소를 통한 무응답 대체법

A Multiple Imputation for Reducing Outlier Effect

  • 김만겸 (한국외국어대학교 통계학과) ;
  • 신기일 (한국외국어대학교 통계학과)
  • Kim, Man-Gyeom (Department of statistics, Hankuk University of Foreign Studies) ;
  • Shin, Key-Il (Department of statistics, Hankuk University of Foreign Studies)
  • 투고 : 2014.10.15
  • 심사 : 2014.12.05
  • 발행 : 2014.12.31
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초록

이상점과 무응답이 동시에 존재하는 경우에는 무응답만 있는 경우에 비해 무응답 대체의 성능이 떨어지게 된다. 이러한 경우에는 먼저 이상점을 탐지하고, 탐지된 이상점의 영향력을 축소한 후 무응답 대체를 실시하여야 한다. 본 논문에서는 이상점의 영향력을 축소하여 무응답 대체법의 성능을 향상시키는 방법을 연구하였다. 이를 위해 She and Owen (2011)이 제안한 이상점 탐지법을 살펴보았고, 탐지된 이상점의 영향력을 줄이기 위한 방법으로 흔히 사용되는 가중치 조정법과 이상점 대체법을 살펴보았다. 또한 이상점 처리 방법을 적용한 무응답 대체법을 살펴보았으며 모의실험과 사례분석을 통하여 이상점 영향력 축소 효과를 살펴보았다.

Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.

키워드

참고문헌

  1. Belcher, R (2003). Application of the Hidiroglou-Berthelot method of outlier detection for periodic business surveys, SSC Annual Meeting, Proceeding of the Survey Method Section.
  2. Hidiroglou, M. A. and Berthelot, J.-M. (1986). Statistical editing and imputation for Periodic Business Surveys, Survey Methodology, 12, 73-83.
  3. Kim, J.-Y. and Shin, K.-I. (2013). Multiple Imputation reducing outlier effect using weight adjustment methods, The Korean Journal of Applied Statistics, 26, 635-647. https://doi.org/10.5351/KJAS.2013.26.4.635
  4. Lee, H., Rancourt, E. and Sarndal, C.-E.(1995). Experiment with variance estimation from survey data with imputed value, Journal of Official Statistics, 10, 231-243.
  5. Lee, S.-J. and Shin, K.-I. (2008). A Study on the sensitivity of the BLS Methods, Communications of the Korean Statistical Society, 15, No. 6, 843-858. https://doi.org/10.5351/CKSS.2008.15.6.843
  6. McCullough, M. and Pennington, T. L. (2009). identifying outliers when creating an imputation based for the Qualterly Financial Report,JSM, Section on Survey Research Methods.
  7. Park, D.-I., Kang, H., Han S.-T. and Choi, H. (2013). Comparison study of outlier detection methods in a regression model, Journal of the Korean Data Analysis Society, 15, 177-186.
  8. Ren, R. and Chamber, R. L. (2004). Outlier robust imputation of survey data, Proceeding of ASA Section on Survey Research Methods.
  9. Rubin, D. B. (1987). Multiple imputation for Nonresponse in Survey, New York.
  10. She, Y. and Owen, A. B. (2011). Outlier detection using nonconvex penalized regression, Journal of the American Statistical Association, 106, 626-639. https://doi.org/10.1198/jasa.2011.tm10390