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<p>This paper proposes two innovative and efficient classes of estimators for estimating the population mean utilizing auxiliary information in simple random sampling. The bias and mean squared error of the proposed estimators are derived up to the first order of approximation under the simple random sampling without replacement scheme. The optimal conditions for minimizing the mean squared error of the newly developed estimators are determined. Efficiency conditions are derived by comparing the mean squared error of the proposed and existing estimators. To validate the study, an empirical analysis is conducted using four real population datasets, and a simulation study is conducted with 80,000 iterations. Based on the results from both the empirical and simulation studies, recommendations are made in favor of the suggested estimators.</p>

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Novel and Efficient Population Mean Estimation with Auxiliary Information: A Monte Carlo Simulation Approach

  • 期刊论文

  • 发表日期：

    2026年5月6日

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

摘要

This paper proposes two innovative and efficient classes of estimators for estimating the population mean utilizing auxiliary information in simple random sampling. The bias and mean squared error of the proposed estimators are derived up to the first order of approximation under the simple random sampling without replacement scheme. The optimal conditions for minimizing the mean squared error of the newly developed estimators are determined. Efficiency conditions are derived by comparing the mean squared error of the proposed and existing estimators. To validate the study, an empirical analysis is conducted using four real population datasets, and a simulation study is conducted with 80,000 iterations. Based on the results from both the empirical and simulation studies, recommendations are made in favor of the suggested estimators.