The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Robust multiple imputation method for missings with boundary and outliers

한계와 이상치가 있는 결측치의 로버스트 다중대체 방법

  • Park, Yousung (Department of Statistics, Korea University) ;
  • Oh, Do Young (Department of Statistics, Korea University) ;
  • Kwon, Tae Yeon (Department of International Finance, Hankuk University of Foreign Studies)
  • 박유성 (고려대학교 통계학과) ;
  • 오도영 (고려대학교 통계학과) ;
  • 권태연 (한국외국어대학교 국제금융학과)
  • Received : 2019.10.02
  • Accepted : 2019.11.24
  • Published : 2019.12.31
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Abstract

The problem of missing value imputation for variables in surveys that include item missing becomes complicated if outliers and logical boundary conditions between other survey items cannot be ignored. If there are outliers and boundaries in a variable including missing values, imputed values based on previous regression-based imputation methods are likely to be biased and not meet boundary conditions. In this paper, we approach these difficulties in imputation by combining various robust regression models and multiple imputation methods. Through a simulation study on various scenarios of outliers and boundaries, we find and discuss the optimal combination of robust regression and multiple imputation method.

항목 무응답(item missing)이 발생한 설문조사에서 결측이 포함된 변수에 이상치(outlier)의 존재와 다른 설문문항 항목과의 논리적 한계(boundary) 조건들이 유의미하다면 결측치 대체문제는 매우 복잡해진다. 한계가 있는 결측값들을 포함한 변수에 이상치가 존재하는 경우, 기존의 회귀분석에 근거한 결측치 대체방법은 편향된 대체값 그리고 한계를 만족하지 않은 대체값을 제시할 가능성이 있다. 이에 본 논문은 회귀모형에 기반을 두고 결측치들을 대체를 함에 있어 이상치와 논리적 한계조건이 자료에 존재하는 경우, 다양한 로버스트 회귀모형과 다중대체 방법의 조합을 통해 해결점을 모색하고자 한다. 이를 위해 이들 방법들의 최적의 조합을 다양한 시나리오별로 모의실험을 통하여 찾아보고 이에 대하여 논의하였다.

Keywords

References

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