Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models |
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Authors: | Ufuk Beyaztas Aylin Alin |
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Institution: | 1. Department of Statistics, Dokuz Eylul University, Izmir, Turkey
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Abstract: | In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The performances of the sufficient and conventional JaB methods have been compared for detecting influential observations in linear regression. Comparison is based on two real-world examples and an extensive designed simulation study. Design includes different sample sizes and various modeling scenarios. The results reveal that proposed method is a good competitor for conventional JaB method with less standard error and amount of computation. |
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