10.57647/mathsci.2025.1904.20

Improvement of the Population Mean Using Cosine Robust Regression Estimator

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  • Muhammad Ijaz*1,
  • Muneeba Slaeem1,
  • Sadiah M. Aljeddani2,
  1. Department of Mathematics and Statistics, The University of Haripur, Haripur, Pakistan
  2. Mathematics Department, Al-Lith University College, Umm Al-Qura University, Al-Lith 21961, Saudi Arabia.

Received: 2025-09-25

Revised: 2025-11-16

Accepted: 2025-11-20

Published in Issue 2025-12-31

Copyright (c) 2026 Muhammad Ijaz, Muneeba Slaeem, Sadiah M. Aljeddani (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ijaz, M., Slaeem, M., & Aljeddani, S. M. (2025). Improvement of the Population Mean Using Cosine Robust Regression Estimator. Mathematical Sciences, 19(4 (December 2025). https://doi.org/10.57647/mathsci.2025.1904.20
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Abstract

This paper introduces a novel family of cos-robust regression-type estimators along with special members to estimate the finite population mean under SRSWOR. The new family of estimators is produced by hybridizing the auxiliary information with the cos function. To reduce the impact of outliers, various robust regression techniques, namely Huber’s M- estimation, Mallows’ GM-estimation, Schweppe’s GM-estimation, and SIS GM-estimation, are employed and theoretically compared with the Ordinary Least Squares (OLS) method. Simulated and actual data are used to generate and validate theoretical properties, such as bias and Mean Square Error (MSE). According to the findings, the robust estimators perform better than the conventional OLS approach in terms of MSE and PRE.

Mathematics Subject Classification (2020). 62D05,62G05, 62G35, 62J05

Keywords

  • Auxiliary information,
  • Comparison,
  • Cos function,
  • MSE,
  • Robust statistics,
  • Variance
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