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Abstract <p>This article introduces an innovative adjusted estimator of ratio-type of the explanatory variable’s population mean, utilizing both traditional and unconventional auxiliary parameters. Frequently, data analysis encounters the challenge of outliers. To address this issue, a resilient measure of the auxiliary variable, unaffected by outliers is employed. We derive the bias and mean squared error (MSE) of the suggested estimator till the first-order approximation. An optimal value for the defining scalar is determined, yielding the minimum MSE for this optimal constant. The suggested estimator is scrutinized against the rival estimators. The efficiency conditions of the recommended estimator are obtained over the rival estimators. These efficiency conditions are substantiated through examination with a real dataset as well as simulated datasets, demonstrating an enhancement compared to alternative estimators.</p>

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Naive Modified Ratio Estimator of Population Mean Using Linear Combination of Robust and Non-Robust Auxiliary Parameters

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Mehdi Ali,
  • Subhash Kumar Yadav,
  • Rajesh Kumar Gupta,
  • Surendra Kumar,
  • Sukhvir Singh

摘要

Abstract

This article introduces an innovative adjusted estimator of ratio-type of the explanatory variable’s population mean, utilizing both traditional and unconventional auxiliary parameters. Frequently, data analysis encounters the challenge of outliers. To address this issue, a resilient measure of the auxiliary variable, unaffected by outliers is employed. We derive the bias and mean squared error (MSE) of the suggested estimator till the first-order approximation. An optimal value for the defining scalar is determined, yielding the minimum MSE for this optimal constant. The suggested estimator is scrutinized against the rival estimators. The efficiency conditions of the recommended estimator are obtained over the rival estimators. These efficiency conditions are substantiated through examination with a real dataset as well as simulated datasets, demonstrating an enhancement compared to alternative estimators.