Robust difference-based outlier detection |
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Authors: | Chun Gun Park |
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Affiliation: | Department of Mathematics, Kyonggi University, Suwan, Republic of Korea |
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Abstract: | AbstractIn 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. |
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Keywords: | Difference-based regression least squares leave-one-out approach |
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