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October 9, 2025MathematicsOpen Access

Doubly Robust Estimation of the Finite Population Distribution Function Using Nonprobability Samples

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This analysis proposes a doubly robust estimator for population distribution in nonprobability samples, suggesting improved accuracy.

The proposed doubly robust estimator effectively addresses selection bias in nonprobability samples.
Empirical results indicate stable performance of the estimators across various scenarios, enhancing their applicability.
This work introduces quantile estimators and Woodruff confidence intervals to improve distributional analysis.
A data integration framework combines probability and nonprobability samples to produce relevant population indicators.

Kwon et al. (2025) studied this question.

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