Identification of Multiple Outliers in Logistic Regression |
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Authors: | A H M Rahmatullah Imon Ali S Hadi |
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Institution: | 1. Institute for Mathematical Research , University Putra Malaysia , Selangor, Malaysia imon_ru@yahoo.com;3. Department of Mathematics , The American University in Cairo , Cairo, Egypt |
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Abstract: | The use of logistic regression modeling has seen a great deal of attention in the literature in recent years. This includes all aspects of the logistic regression model including the identification of outliers. A variety of methods for the identification of outliers, such as the standardized Pearson residuals, are now available in the literature. These methods, however, are successful only if the data contain a single outlier. In the presence of multiple outliers in the data, which is often the case in practice, these methods fail to detect the outliers. This is due to the well-known problems of masking (false negative) and swamping (false positive) effects. In this article, we propose a new method for the identification of multiple outliers in logistic regression. We develop a generalized version of standardized Pearson residuals based on group deletion and then propose a technique for identifying multiple outliers. The performance of the proposed method is then investigated through several examples. |
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Keywords: | Generalized standardized Pearson residuals Group deletion Logistic regression Masking Outliers Pearson residuals |
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