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dc.contributor.authorHuang, Chien-Chia Liamen_US
dc.contributor.authorJou, Yow-Jenen_US
dc.contributor.authorCho, Hsun-Jungen_US
dc.date.accessioned2017-04-21T06:56:16Z-
dc.date.available2017-04-21T06:56:16Z-
dc.date.issued2017en_US
dc.identifier.issn0361-0926en_US
dc.identifier.urihttp://dx.doi.org/10.1080/03610926.2015.1060340en_US
dc.identifier.urihttp://hdl.handle.net/11536/133323-
dc.description.abstractIn this study, we investigate linear regression having both heteroskedasticity and collinearity problems. We discuss the properties related to the perturbation method. Important observations are summarized as theorems. We then prove the main result that states the heteroskedasticity-robust variances can be improved and that the resulting bias is minimized by using the matrix perturbation method. We analyze a practical example for validation of the method.en_US
dc.language.isoen_USen_US
dc.subjectCollinearityen_US
dc.subjectlinear regressionen_US
dc.subjectmatrix theoryen_US
dc.subjectoptimizationen_US
dc.titleVIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearityen_US
dc.identifier.doi10.1080/03610926.2015.1060340en_US
dc.identifier.journalCOMMUNICATIONS IN STATISTICS-THEORY AND METHODSen_US
dc.citation.volume46en_US
dc.citation.issue7en_US
dc.citation.spage3255en_US
dc.citation.epage3263en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000392412400012en_US
Appears in Collections:Articles