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http://dx.doi.org/10.5351/KJAS.2021.34.6.923

Bias corrected non-response estimation using nonparametric function estimation of super population model  

Sim, Joo-Yong (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.34, no.6, 2021 , pp. 923-936 More about this Journal
Abstract
A large number of non-responses are occurring in the sample survey, and various methods have been developed to deal with them appropriately. In particular, the bias caused by non-ignorable non-response greatly reduces the accuracy of estimation and makes non-response processing difficult. Recently, Chung and Shin (2017, 2020) proposed an estimator that improves the accuracy of estimation using parametric super-population model and response rate model. In this study, we suggested a bias corrected non-response mean estimator using a nonparametric function generalizing the form of a parametric super-population model. We confirmed the superiority of the proposed estimator through simulation studies.
Keywords
propensity score; sample distribution; population distribution; local linear regression;
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