Doubly Robust Quantile Estimation for Finite Populations with Non-Probability Samples

Music City Center 

The growing use of non-probability samples in survey research highlights the need for robust methods to control selection bias. Quantiles capture distributional characteristics that mean-based analyses often overlook, yet existing methods primarily focus on means or totals, leaving a gap in rigorous quantile estimation. Current bias mitigation approaches rely on model-based frameworks, which can degrade in performance when misspecified. This paper introduces a doubly robust quantile estimator that is asymptotically unbiased under misspecification of either the outcome or selection model. Our method constructs a robust distribution function and is evaluated through simulations and an application to the Korean Household Financial Welfare Survey. Unlike existing approaches, this is new doubly robust estimator designed for distribution functions. Resampling techniques are employed to construct confidence intervals. Results confirm its effectiveness for quantile estimation in non-probability sample surveys.

non-probability sample

quantile

doubly robust estimator

difference estimator

data integration 

Survey Research Methods Section