Document Type

Article

Original Publication Date

2011

Journal/Book/Conference Title

Advances in Operations Research

Volume

2011

Issue

Article ID 263762, 18 pages

DOI

10.1155/2011/263762

Comments

Originally published open access online through Hindawi at http://dx.doi.org/10.1155/2011/263762

Date of Submission

July 2014

Abstract

Assessing the linear relationship between a set of continuous predictors and a continuous response is a well-studied problem in statistics and data mining. L2-based methods such as ordinary least squares and orthogonal regression can be used to determine this relationship. However, both of these methods become impaired when influential values are present. This problem becomes compounded when outliers confound standard diagnostics. This work proposes an L1-norm orthogonal regression method (L1OR) formulated as a nonconvex optimization problem. Solution strategies for finding globally optimal solutions are presented. Simulation studies are conducted to assess the resistance of the method to outliers and the consistency of the method. The method is also applied to real-world data arising from an environmental science application.

Rights

Copyright © 2011 J. Paul Brooks and Edward L. Boone. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Statistical Sciences and Operations Research Faculty Publications

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