High-Dimensional Regression Under Correlated Design: An Extensive Simulation Study

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dc.authoridYıldırım, Gökhan/0000-0003-4399-7843
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201;
dc.authorwosidYıldırım, Gökhan/Z-4005-2019
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.contributor.authorAhmed, S. Ejaz
dc.contributor.authorKim, Hwanwoo
dc.contributor.authorYildirim, Gokhan
dc.contributor.authorYuzbasi, Bahadir
dc.date.accessioned2024-08-04T20:58:58Z
dc.date.available2024-08-04T20:58:58Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description25th International Workshop on Matrices and Statistics (IWMS) -- JUN 06-09, 2016 -- Funchal, PORTUGALen_US
dc.description.abstractRegression problems where the number of predictors, p, exceeds the number of responses, n, have become increasingly important in many diverse fields in the last couple of decades. In the classical case of small p and large n, the least squares estimator is a practical and effective tool for estimating the model parameters. However, in this so-called Big Data era, models have the characteristic that p is much larger than n. Statisticians have developed a number of regression techniques for dealing with such problems, such as the Lasso by Tibshirani (J R Stat Soc Ser B Stat Methodol 58:267-288, 1996), the SCAD by Fan and Li (J Am Stat Assoc 96(456):1348- 1360, 2001), the LARS algorithm by Efron et al. (Ann Stat 32(2):407-499, 2004), the MCP estimator by Zhang (Ann Stat. 38:894-942, 2010), and a tuning-free regression algorithm by Chatterjee (High dimensional regression and matrix estimation without tuning parameters, 2015, https://arxiv.org/abs/1510.07294). In this paper, we investigate the relative performances of some of these methods for parameter estimation and variable selection through analyzing real and synthetic data sets. By an extensive Monte Carlo simulation study, we also compare the relative performance of proposed methods under correlated design matrix.en_US
dc.description.sponsorshipUniv Madeira, Inst Politecnico Tomaren_US
dc.description.sponsorshipNatural Sciences and the Engineering Research Council of Canada (NSERC)en_US
dc.description.sponsorshipThe research of S. Ejaz Ahmed is supported by the Natural Sciences and the Engineering Research Council of Canada (NSERC).en_US
dc.identifier.doi10.1007/978-3-030-17519-1_11
dc.identifier.endpage175en_US
dc.identifier.isbn978-3-030-17519-1
dc.identifier.isbn978-3-030-17518-4
dc.identifier.issn1431-1968
dc.identifier.startpage145en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-17519-1_11
dc.identifier.urihttps://hdl.handle.net/11616/103306
dc.identifier.wosWOS:000588196700011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofMatrices, Statistics and Big Dataen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCorrelated designen_US
dc.subjectPenalized and non-penalized methodsen_US
dc.subjectHigh-dimensional dataen_US
dc.subjectMonte Carloen_US
dc.titleHigh-Dimensional Regression Under Correlated Design: An Extensive Simulation Studyen_US
dc.typeConference Objecten_US

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