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<p>The present study develop a generalized class of sine-based estimators for finite population variance by utilizing available auxiliary information within the stratified random sampling framework. The bias and mean squared error (MSE) expressions for the suggested estimators have been derived under a first-order approximation. The performance assessment and mathematical validation of the proposed estimators are conducted using two empirical datasets along with a simulation study involving three artificial populations. Based on the outcomes of the numerical and simulation experiments, the suggested class of estimators performs better than the other estimators considered with respect to MSE and percentage relative efficiency (PRE). To highlight the performance of the proposed estimator, graphical representations of the results have been included. This study concludes that the developed sine function-based estimator represents a useful contribution to the literature on variance estimation within stratified random sampling. It ensures higher efficiency and accuracy in estimating the population variance, thereby enhancing its applicability in different statistical contexts.</p>

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Estimation of population variance using novel family of sine based estimator in stratified random sampling

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Rajesh Singh,
  • Shobh Nath Tiwari

摘要

The present study develop a generalized class of sine-based estimators for finite population variance by utilizing available auxiliary information within the stratified random sampling framework. The bias and mean squared error (MSE) expressions for the suggested estimators have been derived under a first-order approximation. The performance assessment and mathematical validation of the proposed estimators are conducted using two empirical datasets along with a simulation study involving three artificial populations. Based on the outcomes of the numerical and simulation experiments, the suggested class of estimators performs better than the other estimators considered with respect to MSE and percentage relative efficiency (PRE). To highlight the performance of the proposed estimator, graphical representations of the results have been included. This study concludes that the developed sine function-based estimator represents a useful contribution to the literature on variance estimation within stratified random sampling. It ensures higher efficiency and accuracy in estimating the population variance, thereby enhancing its applicability in different statistical contexts.