標題: A VIF-based optimization model to alleviate collinearity problems in multiple linear regression
作者: 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
Department of Transportation and Logistics Management
Department of Information Management and Finance
關鍵字: Multicollearity;Variance inflation factor;Convex optimization
公開日期: 1-十二月-2014
摘要: 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.
URI: http://dx.doi.org/10.1007/s00180-014-0504-3
http://hdl.handle.net/11536/123879
ISSN: 0943-4062
DOI: 10.1007/s00180-014-0504-3
期刊: COMPUTATIONAL STATISTICS
Volume: 29
起始頁: 1515
結束頁: 1541
顯示於類別:期刊論文


文件中的檔案:

  1. 000345099200007.pdf