Abstract.
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.
Similar content being viewed by others
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Arslan, O., Edlund, O. & Ekblom, H. Algorithms to compute CM- and S-estimates for regression. Metrika 55, 37–51 (2002). https://doi.org/10.1007/s001840200185
Issue Date:
DOI: https://doi.org/10.1007/s001840200185