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Integrated Path Stability Selection.

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Τίτλος: Integrated Path Stability Selection.
Συγγραφείς: Melikechi, Omar1 (AUTHOR) omar.melikechi@gmail.com, Miller, Jeffrey W.1 (AUTHOR)
Πηγή: Journal of the American Statistical Association. Mar2026, Vol. 121 Issue 553, p454-464. 11p.
Θεματικοί όροι: Feature selection, False positive error, Statistical models, Mathematical bounds, Cancer research
Περίληψη: Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose. In this article, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over them. This yields upper bounds on E(FP) that are much stronger than previous bounds, leading to significantly more true positives in practice for the same target E(FP). Furthermore, our method requires no more computation than the original stability selection algorithm. We demonstrate the method on simulations and real data from two cancer studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Integrated Path Stability Selection.
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  Data: <searchLink fieldCode="AR" term="%22Melikechi%2C+Omar%22">Melikechi, Omar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> omar.melikechi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Miller%2C+Jeffrey+W%2E%22">Miller, Jeffrey W.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Statistical+Association%22">Journal of the American Statistical Association</searchLink>. Mar2026, Vol. 121 Issue 553, p454-464. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22False+positive+error%22">False positive error</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+bounds%22">Mathematical bounds</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+research%22">Cancer research</searchLink>
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  Data: Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose. In this article, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over them. This yields upper bounds on E(FP) that are much stronger than previous bounds, leading to significantly more true positives in practice for the same target E(FP). Furthermore, our method requires no more computation than the original stability selection algorithm. We demonstrate the method on simulations and real data from two cancer studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/01621459.2025.2525589
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 454
    Subjects:
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: False positive error
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Mathematical bounds
        Type: general
      – SubjectFull: Cancer research
        Type: general
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      – TitleFull: Integrated Path Stability Selection.
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            – D: 01
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              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 553
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