What question did this study set out to answer?

To develop a novel estimator for cumulative distribution functions (CDFs) and quantiles using both probability and nonprobability survey samples.

April 3, 2026Japanese Journal of Statistics and Data ScienceOpen Access

Survey data integration for distribution function and quantile estimation

View Full Paper

Ask AI

Bookmark

Authors

JFJeremy R. A. FloodNorth Carolina Agricultural and Technical State University SMSayed A. MostafaNorth Carolina Agricultural and Technical State University

Discussion

Member takes

Implication

Novel methods estimate cumulative distribution functions and quantiles from probability and nonprobability samples, highlighting improvements in accuracy.

Key Points

To develop a novel estimator for cumulative distribution functions (CDFs) and quantiles using both probability and nonprobability survey samples.
Proposed a residual-based CDF estimator integrating probability and nonprobability samples.
Leveraged shared covariates from both datasets while utilizing nonprobability samples for response variables.
Used semiparametric methods to train outcome models on nonprobability samples.
Defined quantile estimators based on the developed CDF estimator.
Introduced linearization and bootstrap methods for variance estimation.
Demonstrated favorable performance of estimators over traditional plug-in mass imputation estimators.
Results confirmed the theoretical properties, including established bias and variance.
A real data example illustrated the applicability and effectiveness of the proposed methods.

Cite This Study

Flood et al. (2026) studied this question.

synapsesocial.com/papers/69cf5e2e5a333a821460c59d https://doi.org/10.1007/s42081-026-00338-0

View Full Paper

Ask AI

Bookmark

Implication

Novel methods estimate cumulative distribution functions and quantiles from probability and nonprobability samples, highlighting improvements in accuracy.

Survey data integration for distribution function and quantile estimation

Discussion

Member takes

Implication

Key Points

Cite This Study

Survey data integration for distribution function and quantile estimation

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider