Nowadays bootstrap techniques are used for data analysis in many other fields like engineering,\r\nphysics, meteorology, medicine, biology, and chemistry. In this paper, the robustness ofWu 1986\r\nand Liu 1988�s Wild Bootstrap techniques is examined. The empirical evidences indicate that\r\nthese techniques yield efficient estimates in the presence of heteroscedasticity problem. However,\r\nin the presence of outliers, these estimates are no longer efficient. To remedy this problem, we\r\npropose a Robust Wild Bootstrap for stabilizing the variance of the regression estimates where\r\nheteroscedasticity and outliers occur at the same time. The proposed method is based on the\r\nweighted residuals which incorporate the MM estimator, robust location and scale, and the\r\nbootstrap sampling scheme of Wu 1986 and Liu 1988. The results of this study show that the\r\nproposed method outperforms the existing ones in every respect.
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