Holcomb, Tyler R. and Morari, Manfred (1993) Significance Regression: Robust Regression for Collinear Data. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechCDSTR:1993.006
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This paper examines robust linear multivariable regression from collinear data. A brief review of M-estimators discusses the strengths of this approach for tolerating outliers and/or perturbations in the error distributions. The review reveals that M-estimation may be unreliable if the data exhibit collinearity. Next, significance regression (SR) is discussed. SR is a successful method for treating collinearity but is not robust. A new significance regression algorithm for the weighted-least-squares error criterion (SR-WLS) is developed. Using the weights computed via M-estimation with the SR-WLS algorithm yields an effective method that robustly mollifies collinearity problems. Numerical examples illustrate the main points.
|Item Type:||Report or Paper (Technical Report)|
|Additional Information:||Partial support this research through the Department of Energy, Office of Basic Energy Sciences is gratefully acknowledged.|
|Group:||Control and Dynamical Systems Technical Reports|
|Subject Keywords:||significance regression, biased regression, PLS, multivariable regression, robust regression, collinearity|
|Usage Policy:||You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.|
|Deposited By:||Imported from CaltechCDSTR|
|Deposited On:||28 Aug 2006|
|Last Modified:||26 Dec 2012 14:29|
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