For random vectors taking values in $\mathbb{R}^d$ we introduce a notion of multivariate quantiles defined in terms of a class of sets and study an associated process which we call the generalized quantile process. This process specializes to the well known univariate quantile process. We obtain functional central limit theorems for our generalized quantile process and show that both Gaussian and non-Gaussian limiting processes can arise. A number of interesting example are included
We construct a Generalized p value for testing statistical hypotheses on the comparison of mean vect...
International audienceA popular way to study the tail of a distribution is to consider its extreme q...
In this paper, we suggest a new class of multivariate counting processes which generalizes and exte...
For random vectors taking values in $\mathbb{R}^d$ we introduce a notion of multivariate quantiles d...
AbstractGeneralized linear statistics are a unifying class that contains U-statistics, U-quantiles, ...
We define the quantile set of order α ∈ [1/2, 1) associated to a law P on R d to be the collection o...
This paper investigates regression quantiles(RQ) for unstable autoregressive models. This uniform Ba...
We introduce a novel graphical model termed quantile dependence graph captur-ing the dependence stru...
A description of the weak and strong limiting behaviour of weighted uniform tail empirical and tail ...
AbstractBahadur-Kiefer approximations for generalized quantile processes as defined in Einmahl and M...
AbstractIt is shown here that Bahadur's [Ann. Math. Statist. (1966) 37, 577–580] almost sure (a.s.) ...
Grothaus M, Kondratiev Y, Streit L. Regular generalized functions in Gaussian analysis. INFINITE DIM...
AbstractThis paper investigates regression quantiles (RQ) for unstable autoregressive models. The un...
AbstractStatistical depth functions are being used increasingly in nonparametric multivariate data a...
We present almost sure central limit theorems for stochastic processes whose time parameter ranges o...
We construct a Generalized p value for testing statistical hypotheses on the comparison of mean vect...
International audienceA popular way to study the tail of a distribution is to consider its extreme q...
In this paper, we suggest a new class of multivariate counting processes which generalizes and exte...
For random vectors taking values in $\mathbb{R}^d$ we introduce a notion of multivariate quantiles d...
AbstractGeneralized linear statistics are a unifying class that contains U-statistics, U-quantiles, ...
We define the quantile set of order α ∈ [1/2, 1) associated to a law P on R d to be the collection o...
This paper investigates regression quantiles(RQ) for unstable autoregressive models. This uniform Ba...
We introduce a novel graphical model termed quantile dependence graph captur-ing the dependence stru...
A description of the weak and strong limiting behaviour of weighted uniform tail empirical and tail ...
AbstractBahadur-Kiefer approximations for generalized quantile processes as defined in Einmahl and M...
AbstractIt is shown here that Bahadur's [Ann. Math. Statist. (1966) 37, 577–580] almost sure (a.s.) ...
Grothaus M, Kondratiev Y, Streit L. Regular generalized functions in Gaussian analysis. INFINITE DIM...
AbstractThis paper investigates regression quantiles (RQ) for unstable autoregressive models. The un...
AbstractStatistical depth functions are being used increasingly in nonparametric multivariate data a...
We present almost sure central limit theorems for stochastic processes whose time parameter ranges o...
We construct a Generalized p value for testing statistical hypotheses on the comparison of mean vect...
International audienceA popular way to study the tail of a distribution is to consider its extreme q...
In this paper, we suggest a new class of multivariate counting processes which generalizes and exte...