Sparse-Group Boosting: Unbiased Group and Variable Selection.
For grouped covariates, we propose a framework for boosting that allows for sparsity within and between groups. By using component-wise and group-wise gradient ridge boosting simultaneously with adjusted degrees of freedom or penalty parameters, a model with similar properties as the sparse-group la...
| Publicado en: | American Statistician Vol. 79; no. 2; pp. 184 - 198 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
May2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184595444&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184595444 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: May2025 vid: 79 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 184595444 10.1080/00031305.2024.2408007 ppf: 184 ppct: 14 formats: tig: atl: Sparse-Group Boosting: Unbiased Group and Variable Selection. aug: au: Obster, Fabian Heumann, Christian affil: Department of Business Administration, University of the Bundeswehr Munich, Neubiberg, Germany Department of Statistics, Ludwig Maximilians University Munich, Munich, Germany su: Organizational research Random variables Beta distribution Regularization parameter Degrees of freedom sug: subj: Organizational research Random variables Beta distribution Regularization parameter Degrees of freedom keyword: Group sparsity Group-component-wise gradient descent Ridge regression Group sparsity Group-component-wise gradient descent Ridge regression ab: For grouped covariates, we propose a framework for boosting that allows for sparsity within and between groups. By using component-wise and group-wise gradient ridge boosting simultaneously with adjusted degrees of freedom or penalty parameters, a model with similar properties as the sparse-group lasso can be fitted through boosting. We show that within-group and between-group sparsity can be controlled by a mixing parameter, and discuss similarities and differences to the mixing parameter in the sparse-group lasso. Furthermore, we show under which conditions variable selection on a group or individual variable basis happens and provide selection bounds for the regularization parameters depending solely on the singular values of the design matrix in a boosting iteration of linear Ridge penalized boosting. In special cases, we characterize the selection chance of an individual variable versus a group of variables through a generalized beta prime distribution. With simulations as well as two real datasets from ecological and organizational research data, we show the effectiveness and predictive competitiveness of this novel estimator. The results suggest that in the presence of grouped variables, sparse-group boosting is associated with less biased variable selection and higher predictability compared to component-wise or group-component-wise boosting. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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