Algorithms to Compute CM- and S-Estimates for Regression

O. Arslan (), O. Edlund () and H. Ekblom ()
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O. Arslan: University of Cukurova, Department of Mathematics
O. Edlund: Luleå University of Technology, Department of Mathematics
H. Ekblom: Luleå University of Technology, Department of Mathematics

A chapter in Developments in Robust Statistics, 2003, pp 62-76 from Springer

Abstract: Summary Constrained M-estimators for regression were introduced by Mendes and Tyler (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 (1992).

Keywords: Global Minimum; Feasible Region; Influence Function; Robust Regression; Preparation Phase (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/978-3-642-57338-5_5

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