Robust BCa-JaB method as a diagnostic tool for linear regression models
The Jackknife-after-bootstrap (JaB) technique originally developed by Efron [8] has been proposed as an approach to improve the detection of influential observations in linear regression models by Martin and Roberts [12] and Beyaztas and Alin [2]. The method is based on the use of percentile-method confidence intervals to provide improved cut-off values for several single case-deletion influence measures. In order to improve JaB, we propose using robust versions of Efron [7]'s bias-corrected and accelerated (BCa) bootstrap confidence intervals. In this study, the performances of robust BCa-JaB and conventional JaB methods are compared in the cases of DFFITS, Welsch's distance and modified Cook's distance influence diagnostics. Comparisons are based on both real data examples and through a simulation study. Our results reveal that under a variety of scenarios, our proposed method provides more accurate and reliable results, and it is more robust to masking effects.
Year of publication: |
2014
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Authors: | Beyaztas, Ufuk ; Alin, Aylin ; Martin, Michael A. |
Published in: |
Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 41.2014, 7, p. 1593-1610
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Publisher: |
Taylor & Francis Journals |
Saved in:
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