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Obtaining a threshold for the stewart index and its extension to ridge regression

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dc.contributor.authorSalmerón Gómez, Román
dc.contributor.authorGarcía García, Catalina
dc.contributor.authorRodríguez Sánchez, Ainara
dc.contributor.orcidhttps://orcid.org/0000-0001-9925-9802
dc.date.accessioned2025-06-25T15:18:44Z
dc.date.available2025-06-25T15:18:44Z
dc.date.issued2020-11-20
dc.descriptionThe registered version of this article, first published in “Computational Statistics 36, 1011–1029 (2021)", is available online at the publisher's website: Elsevier, https://doi.org/10.1007/s00180-020-01047-2 La versión registrada de este artículo, publicado por primera vez en “Computational Statistics 36, 1011–1029 (2021)", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1007/s00180-020-01047-2
dc.description.abstractThe linear regression model is widely applied to measure the relationship between a dependent variable and a set of independent variables. When the independent variables are related to each other, it is said that the model presents collinearity. If the relationship is between the intercept and at least one of the independent variables, the collinearity is nonessential, while if the relationship is between the independent variables (excluding the intercept), the collinearity is essential. The Stewart index allows the detection of both types of near multicollinearity. However, to the best of our knowledge, there are no established thresholds for this measure from which to consider that the multicollinearity is worrying. This is the main goal of this paper, which presents a Monte Carlo simulation to relate this measure to the condition number. An additional goal of this paper is to extend the Stewart index for its application after the estimation by ridge regression that is widely applied to estimate model with multicollinearity as an alternative to ordinary least squares (OLS). This extension could be also applied to determine the appropriate value for the ridge factor.en
dc.description.versionversión final
dc.identifier.citationSánchez, A.R., Gómez, R.S. & García, C.G. Obtaining a threshold for the stewart index and its extension to ridge regression. Comput Stat 36, 1011–1029 (2021). https://doi.org/10.1007/s00180-020-01047-2
dc.identifier.doihttps://doi.org/10.1007/s00180-020-01047-2
dc.identifier.issn0943-4062, ISSNe 1613-9658
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26919
dc.journal.titleComputational Statistics
dc.journal.volume36
dc.language.isoen
dc.page.final1029
dc.page.initial1011
dc.publisherSpringer-Verlag GmbH Germany, part of Springer Nature
dc.relation.centerFacultades y escuelas::Facultad de Ciencias Económicas y Empresariales
dc.relation.departmentECONOMÍA APLICADA E HISTORIA ECONÓMICA
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.uriAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.subjectTEORIA ECONOMICA
dc.subjectHISTORIA ECONOMICA
dc.subject.keywordsLinear regressionen
dc.subject.keywordsmulticollinearityen
dc.subject.keywordsridge regressionen
dc.subject.keywordsStewart indexen
dc.titleObtaining a threshold for the stewart index and its extension to ridge regressionen
dc.typeArticle
dc.typejournal article
dspace.entity.typePublication
relation.isAuthorOfPublication44ad71e8-c715-46c4-9ed9-9346720f4d23
relation.isAuthorOfPublication.latestForDiscovery44ad71e8-c715-46c4-9ed9-9346720f4d23
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