Author(s):
1. Stella Ebele Anekwe:
Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
2. Sidney Iheanyi Onyeagu:
Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Abstract:
Outliers in a statistical analysis strongly affect the performance of the ordinary least squares, such outliers need to be detected and extreme outliers deleted. This paper is aimed at proposing a redescending M-estimator, which is more efficient and robust, compared to other existing redescending M-estimators. The proposed method is applied to real life data to verify its effectiveness in detecting and deleting of outliers. The Monte Carlo simulation method is also used to investigate the performance of the newly proposed method. The results from the real life data and the Monte Carlo simulation method show that the proposed method is effective in the detection and deletion of extreme outliers compared to other existing redescending M-estimators.
Page(s):
997-1014
Published:
Journal: Pakistan Journal of Statistics and Operation Research, Volume: 17, Issue: 3, Year: 2021
Keywords:
efficiency
,
outliers
,
Robustness
,
Mestimators
,
Redescending M estimators
References:
References are not available for this document.
Citations
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