Synthetic Data Generation With Incomplete Survey Data Under Informative Sampling
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917548150226944 |
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| author | Almomani, Ayat Chang, Won Hwang, Youngdeok Kim, Young Min Kim, Hang J. |
| author_facet | Almomani, Ayat Chang, Won Hwang, Youngdeok Kim, Young Min Kim, Hang J. |
| contents | We propose a Bayesian framework for data synthesis and imputation in complex survey settings with informative sampling. To address variance underestimation in existing Bayesian approaches and to accommodate the missing data encountered in survey data, we introduce an adaptive weighting scheme for parameter estimation. We show that the proposed weighting yields consistent estimators with an asymptotically valid Godambe information matrix. The framework is flexible, accommodating a broad class of Bayesian models and facilitating practical implementation. Simulation studies demonstrate that the proposed method provides accurate uncertainty quantification for both model parameters and synthetic population inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31440 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Synthetic Data Generation With Incomplete Survey Data Under Informative Sampling Almomani, Ayat Chang, Won Hwang, Youngdeok Kim, Young Min Kim, Hang J. Methodology We propose a Bayesian framework for data synthesis and imputation in complex survey settings with informative sampling. To address variance underestimation in existing Bayesian approaches and to accommodate the missing data encountered in survey data, we introduce an adaptive weighting scheme for parameter estimation. We show that the proposed weighting yields consistent estimators with an asymptotically valid Godambe information matrix. The framework is flexible, accommodating a broad class of Bayesian models and facilitating practical implementation. Simulation studies demonstrate that the proposed method provides accurate uncertainty quantification for both model parameters and synthetic population inference. |
| title | Synthetic Data Generation With Incomplete Survey Data Under Informative Sampling |
| topic | Methodology |
| url | https://arxiv.org/abs/2605.31440 |