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<p>In survey sampling, post-stratification is widely used to improve estimation accuracy when stratification information becomes available only after data collection. This study proposes a new estimator for the population mean in post-stratified sampling that incorporates auxiliary information to enhance estimation efficiency. Building on ratio- and regression-type estimators, the proposed method combines auxiliary variables with post-stratum weights to reduce bias and Mean Squared Error (MSE). Theoretical properties of the estimator are derived using a first-order approximation. Its performance is evaluated through numerical illustrations based on two real populations and one simulated population, and comparisons are made with existing estimators. Under the considered settings, the proposed estimator exhibits lower MSE and higher Percent Relative Efficiency (PRE) than competing methods, indicating improved precision when reliable auxiliary information is available. The results suggest that the estimator is a useful alternative for population mean estimation in post-stratified sampling, with scope for further investigation under different sampling conditions and designs.</p>

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An improved estimation technique for the population mean in post-stratified sampling: applications and simulation-based evaluation

  • 期刊论文

  • 发表日期：

    2026年4月4日

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  • Mukesh Kumar Verma,
  • Rahul Varshney,
  • Subhash Kumar Yadav,
  • Pragya Kumari

摘要

In survey sampling, post-stratification is widely used to improve estimation accuracy when stratification information becomes available only after data collection. This study proposes a new estimator for the population mean in post-stratified sampling that incorporates auxiliary information to enhance estimation efficiency. Building on ratio- and regression-type estimators, the proposed method combines auxiliary variables with post-stratum weights to reduce bias and Mean Squared Error (MSE). Theoretical properties of the estimator are derived using a first-order approximation. Its performance is evaluated through numerical illustrations based on two real populations and one simulated population, and comparisons are made with existing estimators. Under the considered settings, the proposed estimator exhibits lower MSE and higher Percent Relative Efficiency (PRE) than competing methods, indicating improved precision when reliable auxiliary information is available. The results suggest that the estimator is a useful alternative for population mean estimation in post-stratified sampling, with scope for further investigation under different sampling conditions and designs.