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http://dx.doi.org/10.5351/KJAS.2022.35.4.485

Bias corrected imputation method for non-ignorable non-response  

Lee, Min-Ha (Department of Statistics, Hankuk University of Foreign Studies)
Shin, Key-Il (Department of Statistics, Hankuk University of Foreign Studies)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.4, 2022 , pp. 485-499 More about this Journal
Abstract
Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.
Keywords
response probability model; super-population model; propensity score; multivariate imputation by chained equations(MICE);
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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