Algorithms to compute CM- and S-estimates for regression


Creative Commons License

Arslan O., Edlund O., Ekblom H.

METRIKA, cilt.55, ss.37-51, 2002 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55
  • Basım Tarihi: 2002
  • Doi Numarası: 10.1007/s001840200185
  • Dergi Adı: METRIKA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.37-51
  • Çukurova Üniversitesi Adresli: Hayır

Özet

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.