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<p>This study develops two novel, efficient and consistent estimators for estimating the population mean of a study variable by incorporating auxiliary information under the Ranked Set Sampling framework. The expressions for bias and mean squared error are derived up to the first-order approximation. Conditions for which the proposed estimators outperform the usual unbiased estimator and other existing estimators are established in terms of efficiency. A Monte Carlo simulation study using synthetic datasets generated from both symmetric and asymmetric distributions and an empirical analysis based on two real datasets are conducted to evaluate the performance of the proposed estimators. The findings show that the suggested estimators perform better in terms of efficiency.</p>

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Enhanced Mean Estimation in Ranked Set Sampling Using Auxiliary Information: A Simulation Approach

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Neha Mandal,
  • Shakti Prasad,
  • Housila P. Singh

摘要

This study develops two novel, efficient and consistent estimators for estimating the population mean of a study variable by incorporating auxiliary information under the Ranked Set Sampling framework. The expressions for bias and mean squared error are derived up to the first-order approximation. Conditions for which the proposed estimators outperform the usual unbiased estimator and other existing estimators are established in terms of efficiency. A Monte Carlo simulation study using synthetic datasets generated from both symmetric and asymmetric distributions and an empirical analysis based on two real datasets are conducted to evaluate the performance of the proposed estimators. The findings show that the suggested estimators perform better in terms of efficiency.