Shrinkage and penalized estimation in semi-parametric models with multicollinear data

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dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAhmed, S. Ejaz
dc.date.accessioned2024-08-04T20:41:40Z
dc.date.available2024-08-04T20:41:40Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this paper, we consider estimation techniques based on ridge regression when the matrix appears to be ill-conditioned in the partially linear model using kernel smoothing. Furthermore, we consider that the coefficients can be partitioned as is the coefficient vector for main effects, and is the vector for nuisance' effects. We are essentially interested in the estimation of is close to zero. We suggest ridge pretest, ridge shrinkage and ridge positive shrinkage estimators for the above semi-parametric model, and compare its performance with some penalty estimators. In particular, suitability of estimating the nonparametric component based on the kernel smoothing basis function is also explored. Monte Carlo simulation study is used to compare the relative efficiency of proposed estimators, and a real data example is presented to illustrate the usefulness of the suggested methods. Moreover, the asymptotic properties of the proposed estimators are obtained.en_US
dc.description.sponsorshipNatural Sciences and the Engineering Research Council of Canada (NSERC)en_US
dc.description.sponsorshipThe research of Professor S. Ejaz Ahmed is supported by the Natural Sciences and the Engineering Research Council of Canada (NSERC).en_US
dc.identifier.doi10.1080/00949655.2016.1171868
dc.identifier.endpage3561en_US
dc.identifier.issn0094-9655
dc.identifier.issn1563-5163
dc.identifier.issue17en_US
dc.identifier.scopus2-s2.0-84963894179en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage3543en_US
dc.identifier.urihttps://doi.org/10.1080/00949655.2016.1171868
dc.identifier.urihttps://hdl.handle.net/11616/97274
dc.identifier.volume86en_US
dc.identifier.wosWOS:000383335400013en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of Statistical Computation and Simulationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPretest estimationen_US
dc.subjectshrinkage estimationen_US
dc.subjectridge regressionen_US
dc.subjectpenalty estimationen_US
dc.subjectkernel smoothingen_US
dc.subjectasymptotic and simulationen_US
dc.titleShrinkage and penalized estimation in semi-parametric models with multicollinear dataen_US
dc.typeArticleen_US

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