DOI QR코드

DOI QR Code

Two-stage imputation method to handle missing data for categorical response variable

  • Jong-Min Kim (Statistics Discipline, Division of Science and Mathematics, University of Minnesota at Morris) ;
  • Kee-Jae Lee (Department of Information Statistics, Korea National Open University) ;
  • Seung-Joo Lee (Department of Data Science, Cheongju University)
  • 투고 : 2023.08.26
  • 심사 : 2023.09.18
  • 발행 : 2023.11.30

초록

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

키워드

과제정보

We thank the two respected referees, Associated Editor and Editor for constructive and helpful suggestions which led to substantial improvement in the revised version.

참고문헌

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