SEMINAR by Daniel Hlubinka (Charles University)

SEMINAR by Daniel Hlubinka (Charles University, Czech Republic) on "Multivariate rank test: Measure transport approach"

Abstract :

Extending rank-based inference to a multivariate setting such as multiple-output regression or MANOVA with unspecified d-dimensional error density has remained an open problem for more than half a century. None of the many solutions proposed so far is enjoying the combination of distribution-freeness and efficiency that makes rank-based inference a successful tool in the univariate setting. A concept of center-outward multivariate ranks and signs based on measure transportation ideas has been introduced recently. Center-outward ranks and signs are not only distribution-free but achieve in dimension d > 1 the (essential) maximal ancillarity property of traditional univariate ranks. 

In the talk, we recall basic ideas behind the rank tests, we introduce the multivariate center-outward ranks and signs based on the measure transport approach and show that fully distribution-free testing procedures based on center-outward ranks can achieve parametric efficiency. We show the asymptotic normality results required in the construction of such tests in multiple-output regression and MANOVA models.

Simulations and an empirical study demonstrate the excellent performance of the proposed procedures.