Browsing by Author "Huang, Chien-Chia Liam"

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A VIF-based optimization model to alleviate collinearity problems in multiple linear regression
(2014-12-01)
Jou, Yow-Jen
;
Huang, Chien-Chia Liam
;
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
In this paper, we address data collinearity problems in multiple linear regression from an optimization perspective. We propose a novel linearly constrained quadratic programming model, based on the concept of the variance inflation factor (VIF). We employ the perturbation method that involves imposing a general symmetric non-diagonal perturbation matrix on the correlation matrix. The proposed VIF-based model reduces the largest VIF by minimizing the resulting biases. The VIF-based model can mitigate the harm from data collinearity through the reduction in both the condition number and VIFs, meanwhile improving the statistical significance. The resulting estimator has bounded biases under an iterative framework and hence is termed the least accumulative bias estimator. Certain potential statistical properties can be further considered as the side constraints for the proposed model. Various numerical examples validate the proposed approach.
VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity
(2017)
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
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.
VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity
(2017)
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
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.