標題: VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity
作者: Huang, Chien-Chia Liam
Jou, Yow-Jen
Cho, Hsun-Jung
運輸與物流管理系 註:原交通所+運管所
資訊管理與財務金融系 註:原資管所+財金所
Department of Transportation and Logistics Management
Department of Information Management and Finance
關鍵字: Collinearity;linear regression;matrix theory;optimization
公開日期: 2017
摘要: In 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.
URI: http://dx.doi.org/10.1080/03610926.2015.1060340
http://hdl.handle.net/11536/133251
ISSN: 0361-0926
DOI: 10.1080/03610926.2015.1060340
期刊: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Volume: 46
Issue: 7
起始頁: 3255
結束頁: 3263
顯示於類別:期刊論文