Scholarlink

<p>This study aims to refine population mean estimation in stratified sampling by incorporating post-stratification techniques. We propose a new estimator within a stratified random sampling framework that integrates two study variables and two auxiliary variables, enhancing the accuracy of survey estimates. The performance of the proposed estimator is assessed using mean squared error (MSE) and percentage relative efficiency (PRE), demonstrating its superiority over existing estimators. Additionally, Cramer’s Rule is applied to determine optimal estimator values, ensuring computational efficiency. This study includes an empirical analysis, specifically a case study based on biological data. Both theoretical derivations and empirical validations confirm the estimator’s effectiveness, underscoring its practical significance in survey sampling. This advancement provides a robust and efficient approach to improving population mean estimation, making it a valuable tool for researchers and practitioners in sampling theory and applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimal estimation of two population means under stratified sampling

  • 期刊论文

  • 发表日期：

    2025年12月4日

查看全文
  • Mukesh Kumar Verma

摘要

This study aims to refine population mean estimation in stratified sampling by incorporating post-stratification techniques. We propose a new estimator within a stratified random sampling framework that integrates two study variables and two auxiliary variables, enhancing the accuracy of survey estimates. The performance of the proposed estimator is assessed using mean squared error (MSE) and percentage relative efficiency (PRE), demonstrating its superiority over existing estimators. Additionally, Cramer’s Rule is applied to determine optimal estimator values, ensuring computational efficiency. This study includes an empirical analysis, specifically a case study based on biological data. Both theoretical derivations and empirical validations confirm the estimator’s effectiveness, underscoring its practical significance in survey sampling. This advancement provides a robust and efficient approach to improving population mean estimation, making it a valuable tool for researchers and practitioners in sampling theory and applications.