Catherine Vermandele

Serial and nonserial sign-and-rank statistics: Asymptotic representation and asymptotic normality

The classical theory of rank-based inference is entirely based either on ordinary ranks, which do not allow for considering location nor intercept parameters, or on signed ranks, which require an assumption of symmetry.If the median, in the absence of a symmetry assumption, is considered as a location parameter, the maximal invariance property of ordinary ranks is lost to the ranks and the signs.As shown in Hallin and Werker (2003), conditioning on a maximal invariant in such situations is essential if semiparametric efficiency is to be reached.This new maximal invariant thus suggests a new class of statistics, based on ordinary ranks and signs.An asymptotic representation theory a la H ajek is developed here for such statistics, both in the nonserial and in the serial case.The corresponding asymptotic normality results clearly show how the signs are adding a separate contribution to the asymptotic variance, hence, potentially, to asymptotic e ciency.Applications to semiparametric inference in regression and time series models with median restrictions are treated in detail in a companion paper (Hallin, Werker, and Vermandele 2003).

A Highly Efficient Regression Estimator for Skewed and/or Heavy-tailed Distributed Errors

This paper proposes a simple maximum likelihood regression estimator that outperforms Least Squares in terms of efficiency and mean square error for a large number of skewed and/or heavy tailed error distributions.