Multivariate coefficients of variation; Robust statistical tests; Bias correction methods
Abstract :
[en] In the univariate context, coefficients of variation (CV) are widely used to compare the dispersion of a variable in several populations. When the comparison is based on p characteristics however, side-by-side comparison of marginal CV’s may lead to contradictions. In this talk, we present a multivariate coefficient of variation (MCV), defined as the inverse of the Mahalanobis distance between the mean and the origin, whose usefulness is demonstrated in some applications in finance and analytical chemistry. A full inference toolbox is provided for practitioners: several parametric and non-parametric bias-correction methods are suggested and compared, and some exact and approximate confidence intervals are built and analyzed in a simulation study. Finally, in order to meet the practical need to compare MCV’s in K populations, some asymptotic statistical testing procedures are derived, whose finite-sample performance is empirically assessed.
Throughout the talk, the robustness of the techniques will be discussed. As a by-product, a test statistic allowing to reliably compare K univariate CV’s even in presence of outliers will be outlined.
Disciplines :
Mathematics
Author, co-author :
Aerts, Stéphanie ; Université de Liège > HEC-Ecole de gestion : UER > UER Opérations : Informatique de gestion
Haesbroeck, Gentiane ; Université de Liège > Département de mathématique > Statistique mathématique
Language :
English
Title :
A full inference toolbox to measure multivariate relative dispersion