Variable selection with LASSO regression for complex survey data
dc.contributor.author | Iparragirre Letamendia, Amaia | |
dc.contributor.author | Lumley, Thomas | |
dc.contributor.author | Barrio Beraza, Irantzu | |
dc.contributor.author | Arostegui Madariaga, Inmaculada | |
dc.date.accessioned | 2023-06-19T17:35:56Z | |
dc.date.available | 2023-06-19T17:35:56Z | |
dc.date.issued | 2023-12 | |
dc.identifier.citation | Stat 12(1) : (2023) // Article ID e578 | es_ES |
dc.identifier.issn | 2049-1573 | |
dc.identifier.uri | http://hdl.handle.net/10810/61467 | |
dc.description.abstract | Variable selection is an important step to end up with good prediction models. LASSO regression models are one of the most commonly used methods for this purpose, for which cross-validation is the most widely applied validation technique to choose the tuning parameter . Validation techniques in a complex survey framework are closely related to “replicate weights”. However, to our knowledge, they have never been used in a LASSO regression context. Applying LASSO regression models to complex survey data could be challenging. The goal of this paper is twofold. On the one hand, we analyze the performance of replicate weights methods to select the tuning parameter for fitting LASSO regression models to complex survey data. On the other hand, we propose new replicate weights methods for the same purpose. In particular, we propose a new design-based cross-validation method as a combination of the traditional cross-validation and replicate weights. The performance of all these methods has been analyzed and compared by means of an extensive simulation study to the traditional cross-validation technique to select the tuning parameter for LASSO regression models. The results suggest a considerable improvement when the new proposal design-based cross-validation is used instead of the traditional cross-validation. | es_ES |
dc.description.sponsorship | This work was financially supported in part by grants from the Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco IT1456-22 and by the Ministry of Science and Innovation through BCAM Severo Ochoa accreditation CEX2021-001142-S/MICIN/AEI/10.13039/501100011033 and through project PID2020-115882RB-I00/AEI/10.13039/501100011033 funded by Agencia Estatal de Investigación and acronym “S3M1P4R” and also by the Basque Government through the BERC 2022-2025 program. The work of AI was supported by grant PIF18/213. Open Access funding is provided by the University of the Basque Country. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Wiley | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/CEX2021-001142-S | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2020-115882RB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.title | Variable selection with LASSO regression for complex survey data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2023 The Authors. Stat published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://onlinelibrary.wiley.com/doi/full/10.1002/sta4.578 | es_ES |
dc.identifier.doi | 10.1002/sta4.578 | |
dc.departamentoes | Matemáticas | es_ES |
dc.departamentoeu | Matematika | es_ES |
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Except where otherwise noted, this item's license is described as © 2023 The Authors. Stat published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.