Scholarlink

Abstract <p>This study focuses on calculating the population mean using stratified random sampling with an auxiliary variable provided. To minimize mean square error (MSE) and maximize percentage relative efficiency (PRE), the suggested estimator’s MSE is assessed using a linear cost function. A theoretical comparison is made with existing estimators to determine the criteria required for enhanced performance. The best allocation corresponding to the proposed estimator’s MSE is calculated utilizing an integer programming approach combined with the Lagrange multiplier technique. The estimator’s performance is further tested by comparing it to existing approaches on both real-world and simulated datasets. The results show that the suggested estimator regularly beats current alternatives in terms of efficiency.</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

Enhanced Exponential Type Ratio Estimator for Estimating Population Mean in Stratified Random Sampling under Linear Cost Function

  • 期刊论文

  • 发表日期：

    2025年10月1日

查看全文
  • Bhatt Ravi Jitendrakumar,
  • Ashish Kumar,
  • Yashpal Singh Raghav,
  • Monika Saini

摘要

Abstract

This study focuses on calculating the population mean using stratified random sampling with an auxiliary variable provided. To minimize mean square error (MSE) and maximize percentage relative efficiency (PRE), the suggested estimator’s MSE is assessed using a linear cost function. A theoretical comparison is made with existing estimators to determine the criteria required for enhanced performance. The best allocation corresponding to the proposed estimator’s MSE is calculated utilizing an integer programming approach combined with the Lagrange multiplier technique. The estimator’s performance is further tested by comparing it to existing approaches on both real-world and simulated datasets. The results show that the suggested estimator regularly beats current alternatives in terms of efficiency.