A bootstrap based on the estimating equations of the linear model

Abstract

This paper presents a method of bootstrap estimation for the linear model. Rather than resampling from the original sample, as is conventional, the proposed method resamples summands in an estimating function used to produce the original estimate. The method is computationally simpler then existing competitors. However, its main advantage lies in the robustness to heteroscedasticity of the estimators it produces. These estimators are shown to have desirable asymptotic properties under fairly general conditions. A simulation study provides small sample support for the new method.