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<p>This study proposes a generalised novel class of estimators for the estimation of population variance under a two-occasion successive sampling framework, building on the regression-type strategy of Searls (1964). The proposed class integrates both matched and unmatched sample proportions and is derived under an optimum replacement policy to minimize the mean square error (MSE). Analytical expressions for bias and MSE are obtained under first-order approximations. The theoretical results are supported through empirical evaluation based on artificial, real-world, and simulated data sets. Comparative analysis reveals that the proposed class exhibits uniformly superior performance over existing estimators in terms of precision. These results affirm the practical utility of the proposed method in longitudinal and rotational survey designs.</p>

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Variance estimation in two-occasion successive sampling: a generalized novel class of estimators

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Shashi Bhushan,
  • Shailja Pandey

摘要

This study proposes a generalised novel class of estimators for the estimation of population variance under a two-occasion successive sampling framework, building on the regression-type strategy of Searls (1964). The proposed class integrates both matched and unmatched sample proportions and is derived under an optimum replacement policy to minimize the mean square error (MSE). Analytical expressions for bias and MSE are obtained under first-order approximations. The theoretical results are supported through empirical evaluation based on artificial, real-world, and simulated data sets. Comparative analysis reveals that the proposed class exhibits uniformly superior performance over existing estimators in terms of precision. These results affirm the practical utility of the proposed method in longitudinal and rotational survey designs.