New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology

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dc.contributor.authorTaylan, P.
dc.contributor.authorWeber, G. -W.
dc.contributor.authorBeck, A.
dc.date.accessioned2024-04-24T16:24:31Z
dc.date.available2024-04-24T16:24:31Z
dc.date.issued2007
dc.departmentDicle Üniversitesien_US
dc.description.abstractGeneralized additive models belong to modern techniques frorn statistical learning, and are applicable in many areas of prediction, e.g. in financial mathamatics, computational biology, medicine, chemistry and environmental protection. In these models, the expectation of response is linked to the predictors via a link function. These models are fitted through local scoring algorithm using it scatterplot smoother as building blocks proposed by Hastie and Tibshirani (1987). In this article, we first give it short introduction and review. Then, we present a mathematical modeling by splines based on a new clustering approach for the x, their density, and the variation of output y. We contribute to regression with generalized additive models by bounding (penalizing) second-order terms (curvature) of the splines, leading to a more robust approximation. Previously, in [23], we proposed it refining modification and investigation of the backfitting algorithm, applied to additive models. Then, because of drawbacks of the modified backfitting algorithm, we solve this problem using continuous optimization techniques, which will becorne an important complementary technology and alternative to the concept of modified backfitting algorithm. In particular, we model and treat the constrained residual sum of squares by the elegant Framework of conic quadratic programming..en_US
dc.identifier.doi10.1080/02331930701618740
dc.identifier.endpage698en_US
dc.identifier.issn0233-1934
dc.identifier.issn1029-4945
dc.identifier.issue5-6en_US
dc.identifier.scopus2-s2.0-35148825397en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage675en_US
dc.identifier.urihttps://doi.org/10.1080/02331930701618740
dc.identifier.urihttps://hdl.handle.net/11468/16752
dc.identifier.volume56en_US
dc.identifier.wosWOS:000251131200013
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofOptimizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRegressionen_US
dc.subjectGeneralized Additive Modelen_US
dc.subjectStatistical Learningen_US
dc.subjectClustering Separation Of Variablesen_US
dc.subjectDensityen_US
dc.subjectVariationen_US
dc.subjectCurvatureen_US
dc.subjectBackfitting (Gauss-Seidel) Algorithmen_US
dc.subjectPenalty Methodsen_US
dc.subjectClassificationen_US
dc.subjectContinous Optimizationen_US
dc.subjectConic Quadratic Programmingen_US
dc.subjectFinancial Mathematicsen_US
dc.titleNew approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technologyen_US
dc.typeArticleen_US

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