On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization

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dc.contributor.authorTaylan, Pakize
dc.contributor.authorWeber, Gerhard-Wilhelm
dc.contributor.authorLiu, Lian
dc.contributor.authorYerlikaya-Ozkurt, Fatma
dc.date.accessioned2024-04-24T16:10:59Z
dc.date.available2024-04-24T16:10:59Z
dc.date.issued2010
dc.departmentDicle Üniversitesien_US
dc.description.abstractGeneralized linear models are widely used in statistical techniques. As an extension, generalized partial linear models utilize semiparametric methods and augment the usual parametric terms with a single nonparametric component of a continuous covariate. In this paper, after a short introduction, we present our model in the generalized additive context with a focus on the penalized maximum likelihood and the penalized iteratively reweighted least squares (P-IRLS) problem based on B-splines, which is attractive for nonparametric components. Then, we approach solving the P-IRLS problem using continuous optimization techniques. They have come to constitute an important complementary approach, alternative to the penalty methods, with flexibility for choosing the penalty parameter adaptively. In particular, we model and treat the constrained P-IRIS problem by using the elegant framework of conic quadratic programming. The method is illustrated using a small numerical example. (C) 2010 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.camwa.2010.04.040
dc.identifier.endpage143en_US
dc.identifier.issn0898-1221
dc.identifier.issn1873-7668
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-77953127430
dc.identifier.scopusqualityQ1
dc.identifier.startpage134en_US
dc.identifier.urihttps://doi.org/10.1016/j.camwa.2010.04.040
dc.identifier.urihttps://hdl.handle.net/11468/15215
dc.identifier.volume60en_US
dc.identifier.wosWOS:000279297600014
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Mathematics With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGeneralized Partial Linear Modelsen_US
dc.subjectMaximum Likelihooden_US
dc.subjectPenalty Methodsen_US
dc.subjectConic Quadratic Programmingen_US
dc.subjectCmarsen_US
dc.titleOn the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimizationen_US
dc.titleOn the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization
dc.typeArticleen_US

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