Big data analytics: integrating penalty strategies

Basit Öğe Kaydı
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.contributor.authorAhmed, S. Ejaz
dc.contributor.authorYuzbasi, Bahadir
dc.date.accessioned2024-08-04T20:43:01Z
dc.date.available2024-08-04T20:43:01Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractWe present efficient estimation and prediction strategies for the classical multiple regression model when the dimensions of the parameters are larger than the number of observations. These strategies are motivated by penalty estimation and Stein-type estimation procedures. More specifically, we consider the estimation of regression parameters in sparse linear models when some of the predictors may have a very weak influence on the response of interest. In a high-dimensional situation, a number of existing variable selection techniques exists. However, they yield different subset models and may have different numbers of predictors. Generally speaking, the least absolute shrinkage and selection operator (Lasso) approach produces an over-fitted model compared with its competitors, namely the smoothly clipped absolute deviation (SCAD) method and adaptive Lasso (aLasso). Thus, prediction based only on a submodel selected by such methods will be subject to selection bias. In order to minimize the inherited bias, we suggest combining two models to improve the estimation and prediction performance. In the context of two competing models where one model includes more predictors than the other based on relatively aggressive variable selection strategies, we plan to investigate the relative performance of Stein-type shrinkage and penalty estimators. The shrinkage estimator improves the prediction performance of submodels significantly selected from existing Lasso-type variable selection methods. A Monte Carlo simulation study is carried out using the relative mean squared error (RMSE) criterion to appraise the performance of the listed estimators. The proposed strategy is applied to the analysis of several real high-dimensional data sets.en_US
dc.identifier.doi10.1080/17509653.2016.1153252
dc.identifier.endpage115en_US
dc.identifier.issn1750-9653
dc.identifier.issn1750-9661
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85015436161en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage105en_US
dc.identifier.urihttps://doi.org/10.1080/17509653.2016.1153252
dc.identifier.urihttps://hdl.handle.net/11616/97736
dc.identifier.volume11en_US
dc.identifier.wosWOS:000376238900004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofInternational Journal of Management Science and Engineering Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSparse regression modelsen_US
dc.subjectpenalty and shrinkage estimationen_US
dc.subjectestimation strategiesen_US
dc.subjectMonte Carlo simulationen_US
dc.titleBig data analytics: integrating penalty strategiesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu