Thresholding Nonprobability Units in Combined Data for Efficient Domain Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929714218663936 |
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| author | Savitsky, Terrance D. Williams, Matthew R. Gerrshunskaya, Julie Beresovsky, Vladislav |
| author_facet | Savitsky, Terrance D. Williams, Matthew R. Gerrshunskaya, Julie Beresovsky, Vladislav |
| contents | Quasi-randomization approaches estimate latent participation probabilities for units from a nonprobability / convenience sample. Estimation of participation probabilities for convenience units allows their combination with units from the randomized survey sample to form a survey weighted domain estimate. One leverages convenience units for domain estimation under the expectation that estimation precision and bias will improve relative to solely using the survey sample; however, convenience sample units that are very different in their covariate support from the survey sample units may inflate estimation bias or variance. This paper develops a method to threshold or exclude convenience units to minimize the variance of the resulting survey weighted domain estimator. We compare our thresholding method with other thresholding constructions in a simulation study for two classes of datasets based on degree of overlap between survey and convenience samples on covariate support. We reveal that excluding convenience units that each express a low probability of appearing in \emph{both} reference and convenience samples reduces estimation error. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_09524 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Thresholding Nonprobability Units in Combined Data for Efficient Domain Estimation Savitsky, Terrance D. Williams, Matthew R. Gerrshunskaya, Julie Beresovsky, Vladislav Methodology Quasi-randomization approaches estimate latent participation probabilities for units from a nonprobability / convenience sample. Estimation of participation probabilities for convenience units allows their combination with units from the randomized survey sample to form a survey weighted domain estimate. One leverages convenience units for domain estimation under the expectation that estimation precision and bias will improve relative to solely using the survey sample; however, convenience sample units that are very different in their covariate support from the survey sample units may inflate estimation bias or variance. This paper develops a method to threshold or exclude convenience units to minimize the variance of the resulting survey weighted domain estimator. We compare our thresholding method with other thresholding constructions in a simulation study for two classes of datasets based on degree of overlap between survey and convenience samples on covariate support. We reveal that excluding convenience units that each express a low probability of appearing in \emph{both} reference and convenience samples reduces estimation error. |
| title | Thresholding Nonprobability Units in Combined Data for Efficient Domain Estimation |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.09524 |