A Monte Carlo comparison of regression estimators when the error distribution is long-tailed symmetric

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Mutan O. C., ŞENOĞLU B.

Journal of Modern Applied Statistical Methods, cilt.8, sa.1, ss.161-172, 2009 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8 Sayı: 1
  • Basım Tarihi: 2009
  • Doi Numarası: 10.22237/jmasm/1241136780
  • Dergi Adı: Journal of Modern Applied Statistical Methods
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.161-172
  • Anahtar Kelimeler: Least absolute deviations, Long-tailed symmetric, Modified maximum likelihood, Ordinary least squares, Theil's method, Trimmed least squares, Weighted Theil's method, Winsorized least squares
  • Ankara Üniversitesi Adresli: Evet

Özet

The performances of the ordinary least squares (OLS), modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares (WIN), trimmed least squares (TLS), Theil's (Theil) and weighted Theil's (Weighted Theil) estimators are compared under the simple linear regression model in terms of their bias and efficiency when the distribution of error terms is long-tailed symmetric. © 2009 JMASM, Inc.