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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.21276 |
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| _version_ | 1866912914015780864 |
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| author | Bian, Yuan Yi, Grace Y. He, Wenqing |
| author_facet | Bian, Yuan Yi, Grace Y. He, Wenqing |
| contents | Boosting has emerged as a useful machine learning technique over the past three decades, attracting increased attention. Most advancements in this area, however, have primarily focused on numerical implementation procedures, often lacking rigorous theoretical justifications. Moreover, these approaches are generally designed for datasets with fully observed data, and their validity can be compromised by the presence of missing observations. In this paper, we employ semiparametric estimation approaches to develop boosting prediction methods for data with missing responses. We explore two strategies for adjusting the loss functions to account for missingness effects. The proposed methods are implemented using a functional gradient descent algorithm, and their theoretical properties, including algorithm convergence and estimator consistency, are rigorously established. Numerical studies demonstrate that the proposed methods perform well in finite sample settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting prediction with data missing not at random Bian, Yuan Yi, Grace Y. He, Wenqing Methodology Boosting has emerged as a useful machine learning technique over the past three decades, attracting increased attention. Most advancements in this area, however, have primarily focused on numerical implementation procedures, often lacking rigorous theoretical justifications. Moreover, these approaches are generally designed for datasets with fully observed data, and their validity can be compromised by the presence of missing observations. In this paper, we employ semiparametric estimation approaches to develop boosting prediction methods for data with missing responses. We explore two strategies for adjusting the loss functions to account for missingness effects. The proposed methods are implemented using a functional gradient descent algorithm, and their theoretical properties, including algorithm convergence and estimator consistency, are rigorously established. Numerical studies demonstrate that the proposed methods perform well in finite sample settings. |
| title | Boosting prediction with data missing not at random |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.21276 |