Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Doubly Robust Imputation Using Auxiliary Information

보조 정보에 의한 이중적 로버스트 대체법

  • Park, Hyeon-Ah (Department of Statistics, Seoul National University) ;
  • Jeon, Jong-Woo (Department of Statistics, Seoul National University) ;
  • Na, Seong-Ryong (Department of Information and Statistics, Yonsei University)
  • Received : 20100600
  • Accepted : 20100800
  • Published : 2011.01.30
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Abstract

Ratio and regression imputations depend on the model of a survey variable and the relation between the survey variable and auxiliary variables. If the model is not true, the unbiasedness of the estimator using the ratio or regression imputation cannot be guaranteed. In this paper, we develop the doubly robust imputation, which satisfies the approximate unbiasedness of the estimator, whether the model assumption is valid or not. The proposed imputation increases the efficiency of estimation by using the population information of the auxiliary variables. The simulation study establishes the theoretical results of this paper.

비대체와 회귀대체는 조사변수의 모형과 조사변수와 보조변수의 관계에 의존하며 모형이 성립되지 않는 경우 이들 대체법을 이용한 추정량의 불편성은 보장되지 않는다. 본 연구에서는 모형이 성립되지 않는 경우에도 추정량의 근사적 불편성이 성립되는 로버스트 대체법을 개발한다. 대체법 개발시 보조변수의 모수 정보를 이용하여 추정량의 효율 증대를 가져오게 한다. 모의실험을 실시하여 본 연구에 대한 이론적 결과의 타당성을 보인다.

Keywords

References

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