Estimating parameters in one-way analysis of covariance model with short-tailed symmetric error distributions

ŞENOĞLU B.

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, cilt.201, sa.1, ss.275-283, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 201 Sayı: 1
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.cam.2006.02.019
  • Dergi Adı: JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.275-283
  • Anahtar Kelimeler: covariance analysis, modified likelihood, short-tailed symmetric family, non-normality, efficiency, EXPERIMENTAL-DESIGN, MAXIMUM-LIKELIHOOD
  • Ankara Üniversitesi Adresli: Evet

Özet

We consider one-way analysis of covariance (ANCOVA) model with a single covariate when the distribution of error terms are short-tailed symmetric. The maximum likelihood (ML) estimators of the parameters are intractable. We, therefore, employ a simple method known as modified maximum likelihood (MML) to derive the estimators of the model parameters. The method is based on linearization of the intractable terms in likelihood equations. Incorporating these linearizations in the maximum likelihood, we get the modified likelihood equations. Then the MML estimators which are the solutions of these modified equations are obtained. Computer simulations were performed to investigate the efficiencies of the proposed estimators. The simulation results show that the proposed estimators are remarkably efficient compared with the conventional least squares (LS) estimators. (c) 2006 Elsevier B.V. All rights reserved.