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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09097 |
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| _version_ | 1866913871541829632 |
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| author | Nouraie, Mahdi Muller, Samuel |
| author_facet | Nouraie, Mahdi Muller, Samuel |
| contents | Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection. This paper seeks to broaden the use of an established stability estimator to evaluate the overall stability of the stability selection results, moving beyond single-variable analysis. We suggest that the stability estimator offers two advantages: it can serve as a reference to reflect the robustness of the results obtained, and it can help identify a Pareto optimal regularization value to improve stability. By determining the regularization value, we calibrate key stability selection parameters, namely, the decision-making threshold and the expected number of falsely selected variables, within established theoretical bounds. In addition, the convergence of stability values over successive sub-samples sheds light on the required number of sub-samples addressing a notable gap in prior studies. The \texttt{stabplot} R package is developed to facilitate the use of the methodology featured in this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Selection Stability of Stability Selection and Its Applications Nouraie, Mahdi Muller, Samuel Methodology Computation Machine Learning Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection. This paper seeks to broaden the use of an established stability estimator to evaluate the overall stability of the stability selection results, moving beyond single-variable analysis. We suggest that the stability estimator offers two advantages: it can serve as a reference to reflect the robustness of the results obtained, and it can help identify a Pareto optimal regularization value to improve stability. By determining the regularization value, we calibrate key stability selection parameters, namely, the decision-making threshold and the expected number of falsely selected variables, within established theoretical bounds. In addition, the convergence of stability values over successive sub-samples sheds light on the required number of sub-samples addressing a notable gap in prior studies. The \texttt{stabplot} R package is developed to facilitate the use of the methodology featured in this paper. |
| title | On the Selection Stability of Stability Selection and Its Applications |
| topic | Methodology Computation Machine Learning |
| url | https://arxiv.org/abs/2411.09097 |