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Robust BCa-JaB method as a diagnostic tool for linear regression models

Ufuk Beyaztas, Aylin Alin and Michael A. Martin

Journal of Applied Statistics, 2014, vol. 41, issue 7, 1593-1610

Abstract: 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.

Date: 2014
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DOI: 10.1080/02664763.2014.881788

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