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September 10, 2025Open Access

Improved Estimation of Population Variance Incorporating Auxiliary Information in the Presence of Measurement Errors

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Ignoring measurement error leads to inflated mean squared error, impacting variance estimates significantly.
Proposed estimators outperform traditional methods in addressing measurement error when evaluating datasets.
The study uses benchmark datasets to illustrate improvements in variance estimation under typical survey conditions.
Error-corrected estimation methods are essential for robust data collection and reliable survey sampling outcomes.

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New estimators reduce bias in population variance estimates incorporating auxiliary information, highlighting measurement error implications.

Khalid et al. (2025) studied this question.

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New estimators reduce bias in population variance estimates incorporating auxiliary information, highlighting measurement error implications.