Robust estimation in multiple linear regression model with non-Gaussian noise

Akkaya A., Tiku M. L.

AUTOMATICA, cilt.44, sa.2, ss.407-417, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44 Sayı: 2
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.automatica.2007.06.029
  • Dergi Adı: AUTOMATICA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.407-417
  • Anahtar Kelimeler: linear regression, robustness, data anomaly, modified maximum likelihood, outliers, TIME-SERIES MODELS, MAXIMUM-LIKELIHOOD, BINARY REGRESSION, UNCERTAIN, IDENTIFICATION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

The traditional least squares estimators used in multiple linear regression model are very sensitive to design anomalies. To rectify the situation we propose a reparametrization of the model. We derive modified maximum likelihood estimators and show that they are robust and considerably more efficient than the least squares estimators besides being insensitive to moderate design anomalies. (C) 2007 Elsevier Ltd. All rights reserved.