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Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers

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  • Sohel Rana
  • Habshah Midi
  • A. H. M. R. Imon

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.

Suggested Citation

  • Sohel Rana & Habshah Midi & A. H. M. R. Imon, 2012. "Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-14, February.
  • Handle: RePEc:hin:jnlmpe:730328
    DOI: 10.1155/2012/730328
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    1. Maser, Larissa, 2021. "Job Crafting as Means to Live out One's Calling: An Examination of NPO Employees," Junior Management Science (JUMS), Junior Management Science e. V., vol. 6(2), pages 347-369.
    2. Basri Badyalina & Ani Shabri & Nurkhairany Amyra Mokhtar & Mohamad Faizal Ramli & Muhammad Majid & Muhammad Yassar Yusri, 2021. "Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(6), pages 1-57, December.

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