Robust difference-based outlier detection

Chun Gun Park and Inyoung Kim

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 22, 5553-5577

Abstract: In this paper, we propose an outlier-detection approach that uses the properties of an intercept estimator in a difference-based regression model (DBRM) that we first introduce. This DBRM uses multiple linear regression, and invented it to detect outliers in a multiple linear regression. Our outlier-detection approach uses only the intercept; it does not require estimates for the other parameters in the DBRM. In this paper, we first employed a difference-based intercept estimator to study the outlier-detection problem in a multiple regression model. We compared our approach with several existing methods in a simulation study and the results suggest that our approach outperformed the others. We also demonstrated the advantage of our approach using a real data application. Our approach can extend to nonparametric regression models for outliers detection.

Date: 2020
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DOI: 10.1080/03610926.2019.1620278

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