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Global non-smooth optimization in robust multivariate regression
journal contribution
posted on 2013-02-01, 00:00 authored by Gleb BeliakovGleb Beliakov, Andrei KelarevRobust regression in statistics leads to challenging optimization problems. Here, we study one such problem, in which the objective is non-smooth, non-convex and expensive to calculate. We study the numerical performance of several derivative-free optimization algorithms with the aim of computing robust multivariate estimators. Our experiences demonstrate that the existing algorithms often fail to deliver optimal solutions. We introduce three new methods that use Powell's derivative-free algorithm. The proposed methods are reliable and can be used when processing very large data sets containing outliers.
History
Journal
Optimization methods and softwareVolume
28Issue
1Pagination
124 - 138Publisher
Taylor & FrancisLocation
Essex, Eng.ISSN
1055-6788eISSN
1029-4937Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2013, Taylor & FrancisUsage metrics
Keywords
global optimizationhigh-breakdown regressionleast trimmed squaresnon-smooth optimizationrobust regressionScience & TechnologyTechnologyPhysical SciencesComputer Science, Software EngineeringOperations Research & Management ScienceMathematics, AppliedComputer ScienceMathematicsCUTTING ANGLE METHODSQUARES REGRESSIONOUTLIER DETECTIONMINIMIZATIONALGORITHMSESTIMATORMAXIMUMSIMPLEXComputation Theory and Mathematics
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