On the Iteratively Reweighted Rank Regression Estimator

Abstract

The finite sample performance of the rank estimator of regression coefficients obtained using the iteratively reweighted least squares (IRLS) of Sievers and Abebe (2004) is evaluated. Efficiency comparisons show that the IRLS method does quite well in comparison to least squares or the traditional rank estimates in cases of moderate-tailed error distributions; however, the IRLS method does not appear to be suitable for heavy-tailed data. Moreover, our results show that the IRLS estimator will have an unbounded influence function even if we use an initial estimator with a bounded influence function.