Pre-validation Revisited
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909619484360704 |
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| author | Shang, Jing Chatterjee, Sourav Hastie, Trevor Tibshirani, Robert |
| author_facet | Shang, Jing Chatterjee, Sourav Hastie, Trevor Tibshirani, Robert |
| contents | Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of the resulting test statistic for the pre-validated predictor deviates from a standard Normal, hence leads to issues in hypothesis testing. In this paper, we revisit the pre-validation procedure and extend the problem formulation without any independence assumption on the two feature sets. We propose not only an analytical distribution of the test statistic for the pre-validated predictor under certain models, but also a generic bootstrap procedure to conduct inference. We show properties and benefits of pre-validation in prediction, inference and error estimation by simulations and applications, including analysis of a breast cancer study and a synthetic GWAS example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pre-validation Revisited Shang, Jing Chatterjee, Sourav Hastie, Trevor Tibshirani, Robert Methodology Machine Learning Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of the resulting test statistic for the pre-validated predictor deviates from a standard Normal, hence leads to issues in hypothesis testing. In this paper, we revisit the pre-validation procedure and extend the problem formulation without any independence assumption on the two feature sets. We propose not only an analytical distribution of the test statistic for the pre-validated predictor under certain models, but also a generic bootstrap procedure to conduct inference. We show properties and benefits of pre-validation in prediction, inference and error estimation by simulations and applications, including analysis of a breast cancer study and a synthetic GWAS example. |
| title | Pre-validation Revisited |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2505.14985 |