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Improved regression in ratio type estimators based on robust M-estimation

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  • Khalid Ul Islam Rather
  • Eda Gizem Koçyiğit
  • Ronald Onyango
  • Cem Kadilar

Abstract

In this article, a new robust ratio type estimator using the Uk’s redescending M-estimator is proposed for the estimation of the finite population mean in the simple random sampling (SRS) when there are outliers in the dataset. The mean square error (MSE) equation of the proposed estimator is obtained using the first order of approximation and it has been compared with the traditional ratio-type estimators in the literature, robust regression estimators, and other existing redescending M-estimators. A real-life data and simulation study are used to justify the efficiency of the proposed estimators. It has been shown that the proposed estimator is more efficient than other estimators in the literature on both simulation and real data studies.

Suggested Citation

  • Khalid Ul Islam Rather & Eda Gizem Koçyiğit & Ronald Onyango & Cem Kadilar, 2022. "Improved regression in ratio type estimators based on robust M-estimation," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0278868
    DOI: 10.1371/journal.pone.0278868
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    References listed on IDEAS

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    1. Shashi Bhushan & Anoop Kumar, 2022. "Novel Log Type Class Of Estimators Under Ranked Set Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 421-447, May.
    2. Seyab Yasin & Sultan Salem & Hamdi Ayed & Shahid Kamal & Muhammad Suhail & Yousaf Ali Khan, 2021. "Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-24, September.
    3. Shashi Bhushan & Anoop Kumar, 2022. "Correction to: Novel Log Type Class of Estimators Under Ranked Set Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 448-448, May.
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