Robust Regression-Based Ratio-Type Estimators in Simple, Ranked Set, and Median Ranked Set Sampling: Applications in Sustainable Agriculture

Articles in Press

Document Type : Research Article

Authors

1 Department of Statistics, Government College University Faisalabad, Pakistan.

2 Department of Statistics, Government College University, Faisalabad, Pakistan.

3 College of Statistical Sciences, University of the Punjab, Lahore.

4 Business School, NingboTech University, Ningbo, 315100, Zhejiang, China.

10.24200/sci.2026.66664.10176

Abstract

Recently, researchers have developed robust ratio-type estimators for estimating the population mean under simple random sampling. These estimators exploit robust regression techniques such as Least Absolute Deviation (LAD), Huber-M, Huber-MM, Tukey-M, Least Trimmed Squares (LTS), and Least Median of Squares (LMS). The present study aims to introduce a novel and efficient class of robust regression-based ratio-type estimators for estimating the finite population mean under simple random sampling (SRS), ranked set sampling (RSS), and median ranked set sampling (MRSS) schemes. The proposed estimators exhibit superior reliability and efficiency compared to competing methods across all three sampling designs. The mathematical properties of the proposed estimators are analytically investigated, including derivations of bias and mean squared error. Furthermore, Monte Carlo simulation studies are conducted using two real agricultural field populations to assess the empirical performance of the proposed estimators. The numerical results clearly demonstrate the effectiveness and improved efficiency of the proposed estimators relative to existing estimators. 

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 24 July 2026

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History

  • Receive Date: 03 April 2025
  • Revise Date: 27 January 2026
  • Accept Date: 01 June 2026

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