Spline based sparseness and smoothness for partially nonlinear model via c-fused lasso

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dc.authorid0000-0001-7204-8861en_US
dc.authorid0000-0002-8633-1980en_US
dc.contributor.authorTaylan, Pakize
dc.contributor.authorÖzkurt, Fatma Yerlikaya
dc.contributor.authorTez, Müjgan
dc.date.accessioned2025-02-20T10:40:01Z
dc.date.available2025-02-20T10:40:01Z
dc.date.issued2025en_US
dc.departmentDicle Üniversitesi, Fen Fakültesi, Matematik Bölümüen_US
dc.description.abstractOne of the most beneficial and widely used models for data analysis are partially nonlinear models (PNLRM), which consists of parametric and nonparametric components. Since the model includes the coefficients of both the parametric and nonparametric parts, the complexity of the model will be high and its interpretation will be very difficult. In this study, we propose a procedure that not only achieves sparseness, but also smoothness for PNLRM to obtain a simpler model that better explains the relationship between the response and covariates. Thus, the fused Lasso problem is taken into account where nonparametric components are expressed as a spline basis function, and then the Fused Lasso estimation problem is built and expressed in terms of conic quadratic programming. Applications are conducted to evaluate the performance of the proposed method by considering commonly utilized measures. Promising results are obtained, especially in the data with nonlinearly correlated variables.en_US
dc.identifier.citationTaylan, P., Yerlikaya, F. Ö. ve Tez, M. (2025). Spline based sparseness and smoothness for partially nonlinear model via c-fused lasso. Journal of Industrial and Management Optimization, 21(2), 1120-1144.en_US
dc.identifier.endpage1144en_US
dc.identifier.issn1547-5816
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85210097262
dc.identifier.scopusqualityQ2
dc.identifier.startpage1120en_US
dc.identifier.urihttps://www.aimsciences.org//article/doi/10.3934/jimo.2024118
dc.identifier.urihttps://hdl.handle.net/11468/29472
dc.identifier.volume21en_US
dc.identifier.wosWOS:001309157500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTaylan, Pakize
dc.language.isoenen_US
dc.publisherAmerican Institute of Mathematical Sciencesen_US
dc.relation.ispartofJournal of Industrial and Management Optimization
dc.relation.isversionof10.3934/jimo.2024118en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectContinuous optimizationen_US
dc.subjectEstimationen_US
dc.subjectFused Lassoen_US
dc.subjectNonlinear modelen_US
dc.subjectNonparametric regressionen_US
dc.subjectSpline functionen_US
dc.titleSpline based sparseness and smoothness for partially nonlinear model via c-fused lassoen_US
dc.titleSpline based sparseness and smoothness for partially nonlinear model via c-fused lasso
dc.typeArticleen_US

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