Statistical Robustness: One View of its use in Applications Today

Users of statistical packages need to be aware of the influence that outlying data points can have on their statistical analyses.Robust procedures provide formal methods to spot these outliers and reduce their influence.Although a few robust procedures are mentioned in this article, one is emphasized; it is motivated by maximum likelihood estimation to make it seem more natural.Use of this procedure in regression problems is considered in some detail, and an approximate error structure is stated for the robust estimates of the regression coefficients.A few examples are given.A suggestion of how these techniques should be implemented in practice is included.