{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-integration-of-non-probability-and","title":"Data integration of non-probability and probability samples with predictive mean matching","arxiv_id":"2403.13750","date":"2024-03-20","proceeding":null,"authors":["Aniela Czerniawska","Piotr Chlebicki","Łukasz Chrostowski","Maciej Beręsewicz"],"abstract":"We study deterministic predictive mean matching mass imputation estimators to integrate data from probability and non-probability samples. We consider two approaches: predicted-to-predicted (PMM~A) and predicted-to-observed (PMM~B) matching. We prove the consistency of mean estimators, derive a variance decomposition, and propose estimators of variance. We establish consistency of the PMM~A estimator under model misspecification and underline key differences from the nearest neighbour method. Our PMM~B approach can be employed with non-parametric regression techniques, such as kernel regression, and the analytical expression for variance applies to nearest neighbour matching for non-probability samples. Extensive simulation studies compare properties of the proposed estimators with existing alternatives and examine the effects of model misspecification. The paper concludes with an empirical study on the integration of job vacancy survey and vacancies submitted to public employment offices (admin and online data). Open-source software is available.","url_abs":"https://arxiv.org/abs/2403.13750v2","url_pdf":"https://arxiv.org/pdf/2403.13750v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"data-integration-of-non-probability-and","repo_url":"https://github.com/ncn-foreigners/paper-nonprob-pmm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"data-integration-of-non-probability-and","repo_url":"https://github.com/ncn-foreigners/nonprobsvy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}