Journal article Open Access
On the Robust Parameter Estimation Method for Linear Model with Autocorrelated Errors in the Presence of High Leverage Points and Outliers in the Y-Direction
Dahir Abdi Ali; Midi, H.
In the existence of autocorrelation problems, the Ordinary Least Squares (OLS) estimates become incompetent. The Cochrane - Orcutt Prais -Winsten iterative method (COPW) is the most widely used remedial measure to rectify this problem.
However, this iterative procedure is based on the OLS estimates, which are not resistant and easily influenced by high leverage points (outliers in the -direction) and outliers in they-direction. The COPW based on the MM estimator is developed to remedy both problems of autocorrelation and high leverage points. Nevertheless, the MM estimator does not perform well in the presence of bad leverage points. In this paper, we propose to improvise the Cochrane-OrcuttPrais-Winsten iterative method based on the GM6 estimator so that autocorrelated errors and high leverage points can be rectified. The performance of the COPW-GM6 is scrutinized widely by Monte Carlo simulation and real examples. The results of this study show that the COPW-GM6 is more efficient than the COPW and COPW-MM.
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- Publication date:
- July 5, 2021
- DOI:
-
- Keyword(s):
- Autocorrelation Bad leverage points, Cochrane-Orcutt Prais- Winsten iterative method (COPW) Good leverage points, High leverage points (HLPs), outliers.
- Published in:
- MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES: 14 pp. 505-517.
- Communities:
- License (for files):
- Creative Commons Attribution-NonCommercial-ShareAlike