New Method for Outlier Diagnostics in Linear Regression
JOURNAL OF SURVEYING ENGINEERING, cilt.135, sa.3, ss.85-89, 2009 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 135 Sayı: 3
- Basım Tarihi: 2009
- Doi Numarası: 10.1061/(asce)0733-9453(2009)135:3(85)
- Dergi Adı: JOURNAL OF SURVEYING ENGINEERING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.85-89
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
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%.