Generative modeling for the bootstrap
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910026744987648 |
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| author | Tran, Leon Ye, Ting Ding, Peng Han, Fang |
| author_facet | Tran, Leon Ye, Ting Ding, Peng Han, Fang |
| contents | Generative modeling builds on and substantially advances the classical idea of simulating synthetic data from observed samples. This paper shows that this principle is not only natural but also theoretically well-founded for bootstrap inference: it yields statistically valid confidence intervals that apply simultaneously to both regular and irregular estimators, including settings in which Efron's bootstrap fails. In this sense, the generative modeling-based bootstrap can be viewed as a modern version of the smoothed bootstrap: it could mitigate the curse of dimensionality and remain effective in challenging regimes where estimators may lack root-$n$ consistency or a Gaussian limit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17052 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative modeling for the bootstrap Tran, Leon Ye, Ting Ding, Peng Han, Fang Methodology Econometrics Generative modeling builds on and substantially advances the classical idea of simulating synthetic data from observed samples. This paper shows that this principle is not only natural but also theoretically well-founded for bootstrap inference: it yields statistically valid confidence intervals that apply simultaneously to both regular and irregular estimators, including settings in which Efron's bootstrap fails. In this sense, the generative modeling-based bootstrap can be viewed as a modern version of the smoothed bootstrap: it could mitigate the curse of dimensionality and remain effective in challenging regimes where estimators may lack root-$n$ consistency or a Gaussian limit. |
| title | Generative modeling for the bootstrap |
| topic | Methodology Econometrics |
| url | https://arxiv.org/abs/2602.17052 |