• Title/Summary/Keyword: Hidiroglou-Berthelot 방법

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Multiple Imputation Reducing Outlier Effect using Weight Adjustment Methods (가중치 보정을 이용한 다중대체법)

  • Kim, Jin-Young;Shin, Key-Il
    • The Korean Journal of Applied Statistics
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    • v.26 no.4
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    • pp.635-647
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    • 2013
  • Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.