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Significance Regression: Robust Regression for Collinear Data

Holcomb, Tyler R. and Morari, Manfred (1993) Significance Regression: Robust Regression for Collinear Data. California Institute of Technology , Pasadena, CA. (Unpublished)

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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
Record Number:CaltechCDSTR:1993.006
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Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:28046
Deposited By: Imported from CaltechCDSTR
Deposited On:28 Aug 2006
Last Modified:03 Oct 2019 03:28

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