New Method for Outlier Diagnostics in Linear Regression

HEKİMOĞLU Ş. , Erenoglu R. C.

JOURNAL OF SURVEYING ENGINEERING, vol.135, no.3, pp.85-89, 2009 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 135 Issue: 3
  • Publication Date: 2009
  • Doi Number: 10.1061/(asce)0733-9453(2009)135:3(85)
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.85-89


The detection of the discordant points, i.e., outliers, in linear regression models is a problem, which has been studied extensively. Huber's M-estimation is recommended not only for robust regression but also for detecting outliers. However, M-estimation does not show high performance in detecting outliers for some cases. The aim of this paper is to propose a new method for improving the ability of M-estimation in outlier detection. It consists of the iterative combination of the M-estimator along with a scheme of reducing weights in some observations at random. The theorems proving contribution of the proposed algorithms have also been included. A series of Monte Carlo simulation experiments show that the performance of the new algorithm in the presence of outliers is better than M-estimation alone. By using the new method, the results, on average, improved by about 7%.