Doubly Robust Inference With Nonprobability Survey Samples
By Yilin Chen, Pengfei Li, Changbao Wu. Journal of the American Statistical Association, 2018
172 citations recorded by the source corpus.
Abstract We establish a general framework for statistical inferences with nonprobability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the nonprobability sample, and construct doubly robust estimators for the finite population mean. Variance estimation is discussed under the proposed framework. Results from simulation studies show the robustness and the efficiency of our proposed estimators as compared to existing methods. The proposed method is used to analyze a nonprobability survey sample collected by the Pew Research Center with auxiliary information from the Behavioral Risk Factor Surveillance System and the Current Population Survey. Our results illustrate a general approach to inference with nonprobability samples and highlight the importance and usefulness of auxiliary information from probability survey samples. Supplementary materials for this article are available online.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e990a4adef7f513301a1501dbdc514f3c1ecb26a
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In Statistics and Probability
Citations in the corpus
- Combining non‐probability and probability survey samples through mass imputation (2018, methodology): “3 Comparing mass imputation with inverse probability weighting In the final part of the analysis of the PRC non-probability sample, we make comparisons between mass imputation estimator θ̂I and IPW estimator θ̂IPW of Chen et al. (2019), which is also examined in the simulation study reported in Sect”