Robust Regression Methods : Achieving Small Standard Errors When There Is Heteroscedasticity

@inproceedings{Wilcox2004RobustRM,
  title={Robust Regression Methods : Achieving Small Standard Errors When There Is Heteroscedasticity},
  author={Rand R. Wilcox},
  year={2004}
}
A serious practical problem with the ordinary least squares regression estimator is that it can have a relatively large standard error when the error term is heteroscedastic, even under normality. In practical terms, power can be poor relative to other regression estimators that might be used. This article illustrates the problem and summarizes strategies for dealing with it. Included are new results on the robust estimator recently studied by Anderson and Schumacker (2003).