On the robust rank analysis of linear models with nonsymmetric error distributions

Abstract

The robust analysis of linear models based on R-estimates involves an estimate of a scale parameter which is used in the analysis as a standardizing constant. The consistency of previous estimates of this scale parameter required that the underlying errors be symmetrically distributed. This assumption is not always warranted, for instance in survival models. A new estimate is proposed for the scale parameter and it is shown to be consistent for nonsymmetric and symmetric error distributions. With this new scale estimate, a complete robust analysis of a linear model can be accomplished without assuming symmetry. The small sample properties of the analysis are examined in a Monte Carlo study of several different situations.