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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.17685 |
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| _version_ | 1866912564230750208 |
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| author | Chen, Kai Zhang, Yuqian |
| author_facet | Chen, Kai Zhang, Yuqian |
| contents | In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case of model misspecification. In this work, we challenge such a claim and show that additional unlabeled samples are beneficial in high-dimensional settings. Initially focusing on a dense scenario, we introduce robust semi-supervised estimators for the regression coefficient without relying on sparse structures in the population slope. Even when the true underlying model is linear, we show that leveraging information from large-scale unlabeled data helps reduce estimation bias, thereby improving both estimation accuracy and inference robustness. Moreover, we propose semi-supervised methods with further enhanced efficiency in scenarios with a sparse linear slope. The performance of the proposed methods is demonstrated through extensive numerical studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_17685 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions Chen, Kai Zhang, Yuqian Methodology In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case of model misspecification. In this work, we challenge such a claim and show that additional unlabeled samples are beneficial in high-dimensional settings. Initially focusing on a dense scenario, we introduce robust semi-supervised estimators for the regression coefficient without relying on sparse structures in the population slope. Even when the true underlying model is linear, we show that leveraging information from large-scale unlabeled data helps reduce estimation bias, thereby improving both estimation accuracy and inference robustness. Moreover, we propose semi-supervised methods with further enhanced efficiency in scenarios with a sparse linear slope. The performance of the proposed methods is demonstrated through extensive numerical studies. |
| title | Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions |
| topic | Methodology |
| url | https://arxiv.org/abs/2311.17685 |