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


Akkaya A., Tiku M. L.

AUTOMATICA, vol.44, no.2, pp.407-417, 2008 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 2
  • Publication Date: 2008
  • Doi Number: 10.1016/j.automatica.2007.06.029
  • Journal Name: AUTOMATICA
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.407-417
  • Keywords: linear regression, robustness, data anomaly, modified maximum likelihood, outliers, TIME-SERIES MODELS, MAXIMUM-LIKELIHOOD, BINARY REGRESSION, UNCERTAIN, IDENTIFICATION

Abstract

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.