Multiple linear regression model under nonnormality

Islam M., Tiku M.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, cilt.33, sa.10, ss.2443-2467, 2004 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 10
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1081/sta-200031519
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2443-2467
  • Anahtar Kelimeler: multiple linear regression, modified likelihood, robustness, outliers, M estimators, least squares, nonnormality, hypothesis testing, MAXIMUM-LIKELIHOOD, EXPERIMENTAL-DESIGN, ROBUST, SAMPLES
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.