Lasso regression under stochastic restrictions in linear regression: An application to genomic data

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dc.authoridOzkale, M.Revan/0000-0001-7085-7403
dc.contributor.authorGenc, Murat
dc.contributor.authorOzkale, M. Revan
dc.date.accessioned2025-03-17T12:25:48Z
dc.date.available2025-03-17T12:25:48Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractVariable selection approaches are often employed in high-dimensionality and multicollinearity problems. Since lasso selects variables by shrinking the coefficients, it has extensive use in many fields. On the other, we may sometime have extra information on the model. In this case, the extra information should be considered in the estimation procedure. In this paper, we propose a stochastic restricted lasso estimator in linear regression model which uses the extra information as stochastic linear restrictions. The estimator is a generalization of mixed estimator with L-1 type penalization. We give the coordinate descent algorithm to estimate the coefficient vector of the proposed method and strong rules for the coordinate descent algorithm to discard variables from the model. Also, we propose a method to estimate the tuning parameter. We conduct two real data analyses and simulation studies to compare the new estimator with several estimators including the ridge, lasso and stochastic restricted ridge. The real data analyses and simulation studies show that the new estimator enjoys the automatic variable selection property of the lasso while outperforms standard methods, achieving lower test mean squared error.
dc.identifier.doi10.1080/03610926.2022.2149243
dc.identifier.endpage2839
dc.identifier.issn0361-0926
dc.identifier.issn1532-415X
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85142428704
dc.identifier.scopusqualityQ2
dc.identifier.startpage2816
dc.identifier.urihttps://doi.org/10.1080/03610926.2022.2149243
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1880
dc.identifier.volume53
dc.identifier.wosWOS:000889913400001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofCommunications in Statistics-Theory and Methods
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectBayesian information criterion
dc.subjectcoordinate descent algorithm
dc.subjectgenomic data
dc.subjectlasso
dc.subjectvariable selection
dc.titleLasso regression under stochastic restrictions in linear regression: An application to genomic data
dc.typeArticle

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