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This is a Quantitative Study study.

September 17, 2025Open 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 quantiles in nonprobability samples, indicating its effectiveness alongside traditional methods.

The doubly robust estimator demonstrates asymptotic unbiasedness in estimating distribution functions and quantiles.
Simulation studies using the 2023 Korean Survey showcase the performance and reliability of the proposed methods.
Combining nonprobability samples with probability samples enhances the estimation of distributional indicators.
Bootstrap variance estimation is applied to construct Woodruff confidence intervals, increasing precision.

Kwon et al. (2025) studied this question.

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This analysis proposes a doubly robust estimator for quantiles in nonprobability samples, indicating its effectiveness alongside traditional methods.