Doubly Robust Inference when Combining Probability and Non-Probability Samples with High Dimensional Data
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Summary: We consider integrating a non-probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded concave penalties to select important variables and show selection consistency for general samples. In the second step, we focus on a doubly robust estimator of the finite population mean and re-estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first-step selection error and renders the doubly robust estimator root n consistent if either the sampling probability or the outcome model is correctly specified.
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Other promising approaches include integrating surveys of varying quality, and leveraging the estimated ddc in one outcome to correct bias in others under several scenarios.
“Other promising approaches include integrating surveys of varying quality 53,54 , and leveraging the estimated ddc in one outcome to correct bias in others under several scenarios”
From Unrepresentative big surveys significantly overestimated US vaccine uptake (2021) · cites this paper as [54]
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He, X., Mao, X., & Wang, Z. (2023) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2023.107890
Robust propensity score weighting estimation under missing at random
Wang, H., Kim, J. K., Han, J., & Lee, Y. (2024) · Electronic Journal of Statistics · doi:10.1214/24-ejs2263
Self-citationSuperpopulation model inference for non-probability samples under informative sampling
Liu, Z., Wang, D., & Pan, Y. (2024) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2024.2374056
Alharbi, N., & Cavanaugh, P. F. (2025) · ASEE Northeast Section Meeting · doi:10.18260/1-2-60-54958
Liu, Z., Zhou, Q., & Pan, Y. (2025) · Brazilian Journal of Probability and Statistics · doi:10.1214/25-bjps624
Selection Bias Adjustment by Functional Transfer Learning via Reproducing Kernel Hilbert Space
Wang, Z., Mao, X., Kim, J. K., & Wang, H. (2025) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2025.2515911
Self-citationEfficiency-improved doubly robust estimation with non-confounding predictive covariates
Luo, S., Shi, M., Li, W., Wang, X., & Zhi, G. (2025) · Electronic Journal of Statistics · doi:10.1214/25-ejs2424
Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness
Liu, Z., Xu, J., Li, R., & Pan, Y. (2025) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.70040
Muestreo y sesgo de cobertura en encuestas virtuales: una evaluación mediante simulación estadística
Arreaga, L. M. Q., Rivera, J. A. H., Puma, M. F. V., Bermeo, M. D. R. R., & Chicaiza, S. C. U. (2026) · Horizon Nexus Journal. · doi:10.70881/hnj/v4/n1/110
Survey data integration for distribution function and quantile estimation
Flood, J., & Mostafa, S. A. (2026) · Japanese Journal of Statistics and Data Science · doi:10.1007/s42081-026-00338-0
Gómez-García, L. M., Gras-García, E. M., la Vega‐Taboada, E. D., Duran, N. S., King, A. C., Doueiri, Z. N., Henao, J. M. M., Moncada, C. H., Guzmán, L. A., Riveros, L. M., et al. (2026) · BMC Public Health · doi:10.1186/s12889-026-27578-9
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Pseudo generalized regression estimation for non-probability samples with high-dimensional data
Liu, Z., Liang, Y., Zheng, J., & Pan, Y. (2026) · Metrika · doi:10.1007/s00184-026-01046-1
Ferreira-Neira, J. P. (2026) · Statistical Journal of the IAOS · doi:10.1177/18747655261501357
- Authors
- Yang, S., Kim, J. K., & Song, R.
- Affiliations
- North Carolina State University (US)
- Iowa State University (US)
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-07
- Volume
- 82
- Issue
- 2
- Pages
- 445-465
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- PMC
- PMC7644042
- OpenAlex
- W2921797130
- Cited by
- 73
- References
- 55
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 8 · 2023: 13 · 2022: 11 · 2021: 8 · 2020: 5 · 2019: 1
- FWCI
- 6.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Iowa State University Digital Repository (Iowa State University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1811245)
- Oak Ridge Associated Universities
- National Cancer Institute (P01CA142538, CA142538)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- doubly robust estimation, high-dimensional data, feature selection, finite population mean, selection consistency, root-n consistency, model misspecification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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