A procedure relying on linear programming techniques is developed to compute (regression) quantile regions that have been defined recently. In the location case, this procedure allows for computing halfspace depth regions even beyond dimension two. The corresponding algorithm is described in...

This article defines a meaningful concept of elliptical location quantile with the aid of quantile regression, discusses its basic properties, and suggests its extension to a general regression framework through a locally constant nonparametric approach.

Let I1, I2, . . . , In be a sequence of independent indicator functions de- fined on a probability space (Ω, A, P ). We say that index k is a success time if Ik = 1. The sequence I1, I2, . . . , In is observed sequentially. The objective of this article is to predict the l-th last success, if...

This paper is devoted to the R-estimation problem for the parameter of a stationary ARMA model. The asymptotic uniform linearity of a suitable vector of rank statistics leads to the asymptotic normality of √n-consistent R-estimates resulting from the minimization of the norm of this vector. By...

We consider asymptotic inference for the concentration of directional data. More precisely, wepropose tests for concentration (i) in the low-dimensional case where the sample size n goes to infinity andthe dimension p remains fixed, and (ii) in the high-dimensional case where both n and p become...