Communications for Statistical Applications and Methods

  • 제29권2호
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  • Pages.263-275
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  • 2022
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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A response probability estimation for non-ignorable non-response

  • Chung, Hee Young (Department of Statistics, Hankuk University of Foreign Studies) ;
  • Shin, Key-Il (Department of Statistics, Hankuk University of Foreign Studies)
  • 투고 : 2021.10.25
  • 심사 : 2022.02.25
  • 발행 : 2022.03.31
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초록

Use of appropriate technique for non-response occurring in sample survey improves the accuracy of the estimation. Many studies have been conducted for handling non-ignorable non-response and commonly the response probability is estimated using the propensity score method. Recently, post-stratification method to obtain the response probability proposed by Chung and Shin (2017) reduces the effect of bias and gives a good performance in terms of the MSE. In this study, we propose a new response probability estimation method by combining the propensity score adjustment method using the logistic regression model with post-stratification method used in Chung and Shin (2017). The superiority of the proposed method is confirmed through simulation.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT)(NRF-2021R1F1A1045602).

참고문헌

  1. Bethlehem J (2020). Working with response probabilities, Journal of Official Statistics, 36, 647-674. https://doi.org/10.2478/jos-2020-0033
  2. Chang T and Kott PS (2008). Using calibration weighting to adjust for nonresponse under a plausible model, Biometrika, 95, 555-571. https://doi.org/10.1093/biomet/asn022
  3. Chung HY and Shin KI (2017). Estimation using informative sampling technique when response probability follows exponential function of variable of interest, Korean Journal of Applied Statistics, 30, 993-1004. https://doi.org/10.5351/KJAS.2017.30.6.993
  4. Da Silva DN and Opsomer JD (2006). A kernel smoothing method of adjusting for unit non-response in sample surveys, Canadian Journal of Statistics, 34, 563-579. https://doi.org/10.1002/cjs.5550340402
  5. Da Silva DN and Opsomer JD (2009). Nonparametric propensity weighting for survey nonresponse through local polynomial regression, Survey Methodology, 35, 165-176.
  6. Iannacchinoe VG, Milne JG, and Folsom RE (1991). Response probability weight adjustment using logistic regression. In Proceedings of the Survey Research Methods Section, American Statistical Association, 637-642.
  7. Kim JK and Riddles MK (2012). Some theory for propensity-score-adjustment estimators in survey sampling, Survey Methodology, 38, 157-165.
  8. Kim JK and Yu CL (2011). A semiparametric estimation of mean functionals with nonignorable missing data, Journal of the American Statistical Association, 106, 157-165. https://doi.org/10.1198/jasa.2011.tm10104
  9. Kott PS and Chang T (2010). Using Calibration weighting to adjust for nonignorable unit nonresponse, American Statistical Association, 105, 1265-1275. https://doi.org/10.1198/jasa.2010.tm09016
  10. Min JW and Shin KI (2018). A study on the determination of substrata using the information of exponential response rate by simulation studies, Korean Journal of Applied Statistics, 31, 621-636. https://doi.org/10.5351/KJAS.2018.31.5.621
  11. Pfeffermann D, Krieger AM, and Rinott Y (1998). Parametric distributions of complex survey data under informative probability sampling, Statistica Sinica, 8, 1087-1114.
  12. Pfeffermann D and SverchkovM(2003). Small area estimation under informative sampling, Proceedings of the Survey Research Method Section, American Statistical Asscociation, 3284-3295.
  13. Riddles MK, Kim JK, and Im J (2016). A propensity-score-adjustment method for nonignorable nonresponse, Journal of Survey Statistics and Methodology, 4, 215-245. https://doi.org/10.1093/jssam/smv047