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Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rana, Sohel Midi, Habshah Imon, A. H. M. R. |
| Copyright Year | 2014 |
| Abstract | Nowadays bootstrap techniques are used for data analysis in many other fields like engineering, physics, meteorology, medicine, biology, and chemistry. In this paper, the robustness of Wu 1986 and Liu 1988 ’s Wild Bootstrap techniques is examined. The empirical evidences indicate that these techniques yield efficient estimates in the presence of heteroscedasticity problem. However, in the presence of outliers, these estimates are no longer efficient. To remedy this problem, we propose a Robust Wild Bootstrap for stabilizing the variance of the regression estimates where heteroscedasticity and outliers occur at the same time. The proposed method is based on the weighted residuals which incorporate the MM estimator, robust location and scale, and the bootstrap sampling scheme of Wu 1986 and Liu 1988 . The results of this study show that the proposed method outperforms the existing ones in every respect. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://emis.maths.tcd.ie/EMIS/journals/HOA/MPE/Volume2012/730328.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Bootstrapping (statistics) Estimated Population Parameter Sample Variance Sampling (signal processing) |
| Content Type | Text |
| Resource Type | Article |
