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This problem becomes compounded when outliers confound standard diagnostics. This work proposes an -norm orthogonal regression method (OR) 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.<\/jats:p>","DOI":"10.1155\/2011\/263762","type":"journal-article","created":{"date-parts":[[2011,2,17]],"date-time":"2011-02-17T14:32:12Z","timestamp":1297953132000},"page":"1-18","source":"Crossref","is-referenced-by-count":1,"title":["Outlier-Resistant Orthogonal Regression via the Reformulation-Linearization Technique"],"prefix":"10.1155","volume":"2011","author":[{"given":"J. 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