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Algorithms to compute CM - and S-estimates for regression
Department of Mathematics, Cukurova University.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
2003 (English)In: International Conference on Robust Statistics: ICORS / [ed] Rudolf Dutter; P. Filzmoser; U. Gather; P. J. Pousseeuw, Physica-Verlag Rudolf Liebig GmbH , 2003, 62-76 p.Conference paper, Published paper (Refereed)
Abstract [en]

Constrained M-estimators for regression were introduced by Mendes and Tyler in 1995 as an alternative class of robust regression estimators with high breakdown point and high asymptotic efficiency. To compute the CM-estimate, the global minimum of an objective function with an inequality constraint has to be localized. To find the S-estimate for the same problem, we instead restrict ourselves to the boundary of the feasible region. The algorithm presented for computing CM-estimates can easily be modified to compute S-estimates as well. Testing is carried out with a comparison to the algorithm SURREAL by Ruppert

Place, publisher, year, edition, pages
Physica-Verlag Rudolf Liebig GmbH , 2003. 62-76 p.
Series
Metrika, ISSN 0026-1335 ; 1-2
National Category
Computational Mathematics
Research subject
Scientific Computing
Identifiers
URN: urn:nbn:se:ltu:diva-37223Local ID: b2e030f0-ac80-11db-aeba-000ea68e967bISBN: 3-7908-1518-7 (print)OAI: oai:DiVA.org:ltu-37223DiVA: diva2:1010721
Conference
Developments in robust statistics : 23/07/2001 - 27/07/2001
Note
Godkänd; 2003; 20070122 (evan)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-11-25Bibliographically approved

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Edlund, OveEkblom, Håkan

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