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An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)

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  • Syed Abdul Rehman
  • Javid Shabbir

Abstract

In this study, we address the problem of estimating the finite population mean when the non-response occurs on the characteristics under study. We propose a class of Rao-regression type estimators when ranked set sampling (RSS) procedure is used to collect the data from non-response group only and from both, the response and non-response groups. The information provided on the auxiliary variable is used at both stages i.e., at designing stage and the estimation stage. Expressions for bias and mean square error of the estimators are obtained up to first order of approximation. A comprehensive simulation study is carried out to observe the performances of the estimators under non-response.

Suggested Citation

  • Syed Abdul Rehman & Javid Shabbir, 2022. "An efficient class of estimators for finite population mean in the presence of non-response under ranked set sampling (RSS)," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-14, December.
    • Handle: RePEc:plo:pone00:0277232
    DOI: 10.1371/journal.pone.0277232
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    References listed on IDEAS

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    1. Showkat Ahmad Lone & Mir Subzar & Ankita Sharma, 2021. "Enhanced Estimators of Population Variance with the Use of Supplementary Information in Survey Sampling," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, April.
    2. Malik Muhammad Anas & Muhammad Ali & Ambreen Shafqat & Faisal Shahzad & Kashif Abbass & David Anekeya Alilah, 2021. "L-Moments and Calibration-Based Estimators for Variance Parameter," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, August.
    3. Malik Muhammad Anas & Zhensheng Huang & Usman Shahzad & Tolga Zaman & Shabnam Shahzadi, 2022. "Compromised imputation based mean estimators using robust quantile regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(5), pages 1700-1715, August.
    4. Malik Muhammad Anas & Zhensheng Huang & David Anekeya Alilah & Ambreen Shafqat & Sajjad Hussain, 2021. "Mean Estimators Using Robust Quantile Regression and L-Moments’ Characteristics for Complete and Partial Auxiliary Information," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, July.
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