252 citations · 827 across the 31 of their papers we have counts for
7 papers · 1 filter
Detecting the direction of a signal on high-dimensional spheres: Non-null and Le Cam optimality results
Davy Paindaveine, Thomas Verdebout
We consider one of the most important problems in directional statistics, namely the problem of testing the null hypothesis that the spike direction of a Fisher-von Mises-Lange…
Testing for Principal Component Directions under Weak Identifiability
Davy Paindaveine, Julien Remy, Thomas Verdebout
We consider the problem of testing, on the basis of a -variate Gaussian random sample, the null hypothesis against the alternative ${\cal…
Distance-based Depths for Directional Data
Giuseppe Pandolfo, Davy Paindaveine, Giovanni Porzio
Directional data are constrained to lie on the unit sphere of~ for some~. To address the lack of a natural ordering for such data, depth functions have been…
On the maximal halfspace depth of permutation-invariant distributions on the simplex
Davy Paindaveine, Germain Van Bever
We compute the maximal halfspace depth for a class of permutation-invariant distributions on the probability simplex. The derivations are based on stochastic ordering results that…
On optimal tests for rotational symmetry against new classes of hyperspherical distributions
Eduardo García-Portugués, Davy Paindaveine, Thomas Verdebout
Motivated by the central role played by rotationally symmetric distributions in directional statistics, we consider the problem of testing rotational symmetry on the hypersphere. W…
Tyler shape depth
Davy Paindaveine, Germain Van Bever
In many problems from multivariate analysis, the parameter of interest is a shape matrix, that is, a normalized version of the corresponding scatter or dispersion matrix. In this p…