Semiparametric Efficient Data Integration Using the Dual-Frame Sampling Framework
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912820375846912 |
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| author | Morikawa, Kosuke Kim, Jae Kwang |
| author_facet | Morikawa, Kosuke Kim, Jae Kwang |
| contents | Integrating probability and non-probability samples is increasingly important, yet unknown sampling mechanisms in non-probability sources complicate identification and efficient estimation. We develop semiparametric theory for dual-frame data integration and propose two complementary estimators. The first models the non-probability inclusion probability parametrically and attains the semiparametric efficiency bound. We introduce an identifiability condition based on strong monotonicity that identifies sampling-model parameters without instrumental variables, even under informative (non-ignorable) selection, using auxiliary information from the probability sample; it remains valid without record linkage between samples. The second estimator, motivated by a two-stage sampling approximation, avoids explicit modeling of the non-probability mechanism; though not fully efficient, it is efficient within a restricted augmentation class and is robust to misspecification. Simulations and an application to the Culture and Community in a Time of Crisis public simulation dataset show efficiency gains under correct specification and stable performance under misspecification and weak identification. Methods are implemented in the R package \texttt{dfSEDI}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08707 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semiparametric Efficient Data Integration Using the Dual-Frame Sampling Framework Morikawa, Kosuke Kim, Jae Kwang Methodology Statistics Theory Integrating probability and non-probability samples is increasingly important, yet unknown sampling mechanisms in non-probability sources complicate identification and efficient estimation. We develop semiparametric theory for dual-frame data integration and propose two complementary estimators. The first models the non-probability inclusion probability parametrically and attains the semiparametric efficiency bound. We introduce an identifiability condition based on strong monotonicity that identifies sampling-model parameters without instrumental variables, even under informative (non-ignorable) selection, using auxiliary information from the probability sample; it remains valid without record linkage between samples. The second estimator, motivated by a two-stage sampling approximation, avoids explicit modeling of the non-probability mechanism; though not fully efficient, it is efficient within a restricted augmentation class and is robust to misspecification. Simulations and an application to the Culture and Community in a Time of Crisis public simulation dataset show efficiency gains under correct specification and stable performance under misspecification and weak identification. Methods are implemented in the R package \texttt{dfSEDI}. |
| title | Semiparametric Efficient Data Integration Using the Dual-Frame Sampling Framework |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2601.08707 |