Derived components regression using the BACON algorithm

Authors:
Athanassios Kondylis;Ali S. Hadi
Affiliations:
Statistical Institute, University of Neuchítel, Espace de l'Europe 4, CP 805, 2002 Neuchítel, Switzerland;Department of Mathematics, The American University in Cairo, Egypt and Department of Statistical Sciences, Cornell University, USA
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

Partial least squares and principal components regression are commonly used regularized regression methods which use derived components instead of original predictors. The components are derived from the estimated variance-covariance matrix and regression is run using the least squares. Therefore, they are not robust and a few outliers may have drastic effects on the obtained results. These regression methods are robustified by using the BACON algorithm which provides robust measures for both dispersion and regression. The proposed methods are illustrated by examples and their properties are investigated using both real data and simulation experiments.