Robust Bootstrap for Handling Heteroscedasticity and Outliers in the Presence of HighLeverage Point

Abstract

It's fascinating how researchers are constantly improving regression analysis methods todeal with issues like heteroscedasticity. The  robust MM estimator seems like a smart choicetoenhance the wild bootstrap process for more accurate results in regression analysis.  Researchers are debating the best bootstrap technique for dealing with outliers and heteroscedasticity in linear regression. There is a  push for a more efficient and accurate method, considering the draw backs of the Minimum Volume Ellipsoid approach. The proposal to  replace MVE with ISE in the modified method is a promising step towards better speed, accuracy, and efficiency in robust bootstrapping.  The specific objective of this paper istomodify the existing robust bootstrap technique (WBootMM-GM6-Liu). The  methodology under studied the existing models and compared four existing bootstrap techniques withthemodified version of the  WBootMM-GM6-Liu to ascertain the impact of the modification. The numerical test results revealed that the modified version of the  technique has the least standard errors, bias, and root mean square errors (RSME) and therefore outperforms the existingmodels taking  into account the presence of heteroscedasticity, outliers, and high leveragepoints (HLPs). In the case of further research, this model can  possibly be improved upon based on assessing fixed and random effects with other variables apart from those considered in this paper.