Algorithms to compute CM- and S-estimates for regression

Arslan O., Edlund O., Ekblom H.

International Conference on Robust Statistics (ICOR 2001), Voru, Estonia, 23 - 27 July 2001, pp.62-76 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Voru
  • Country: Estonia
  • Page Numbers: pp.62-76


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).