Improved Estimator for Population Variance using two Auxiliary Information
DOI:
https://doi.org/10.62933/aaky7031Keywords:
Population variance, Exponential-type estimator, Auxiliary Variables, Mean square error, Regression adjustmentAbstract
This paper introduces a novel exponential estimator employing two auxiliary variables to estimate an unknown population variance. The estimator is designed to improve efficiency as compared with existing estimators. The bias and mean squared error (MSE) of the proposed estimator are obtained by a first-order Taylor series expansion. A theoretical comparison with several existing estimators is presented to demonstrate its superiority. To validate the theoretical results, an empirical study is conducted using three population datasets, complemented by a simulation study for cross-validation. The findings indicate that the proposed estimator consistently provides more precise estimates than the existing estimators. Additionally, we introduced a hybrid approach combining the exponential-type estimator with regression adjustment, further reducing bias and MSE under correlated auxiliary information.
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Copyright (c) 2026 Sunil Kumar Yadav, Prof. Rajesh Singh (Author)
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