Outlier detection and robust estimation of scale

Abstract

The strategy of removing points via a backwards-stepping outlier detection procedure and then taking the standard deviation of the remaining points is examined as a robust scale estimator via computer simulations. It is shown that this procedure compares favorably with the most effective robust scale estimators. This is particularly true if the outliers are relatively extreme or follow an asymmetric distribution. It is also shown that this strategy results in an estimator with high breakdown and redescending influence.