242 citations · 778 across the 62 of their papers we have counts for
31 papers · 1 filter
Unrestricted conditional maximum likelihood estimation for a Fréchet regression model
Marcio Reverbel, Johan Segers
Heavy-tailed response variables are routinely modeled as a function of covariates, but the asymptotic theory available for the resulting maximum likelihood estimators sidesteps cer…
An asymptotic expansion of the empirical angular measure for bivariate extremal dependence
Stéphane Lhaut, Johan Segers
The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standar…
Graphical and uniform consistency of estimated optimal transport plans
Johan Segers
A general theory is provided delivering convergence of maximal cyclically monotone mappings containing the supports of coupling measures of sequences of pairs of possibly random pr…
Tail inference using extreme U-statistics
Jochem Oorschot, Johan Segers, Chen Zhou
Extreme U-statistics arise when the kernel of a U-statistic has a high degree but depends only on its arguments through a small number of top order statistics. As the kernel degree…
Measuring dependence between random vectors via optimal transport
Gilles Mordant, Johan Segers
To quantify the dependence between two random vectors of possibly different dimensions, we propose to rely on the properties of the 2-Wasserstein distance. We first propose two coe…
Concentration bounds for the empirical angular measure with statistical learning applications
Stéphan Clémençon, Hamid Jalalzai, Stéphane Lhaut +2
The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standar…
Empirical tail copulas for functional data
John H. J. Einmahl, Johan Segers
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the…
Control variate selection for Monte Carlo integration
Rémi Leluc, François Portier, Johan Segers
Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a multiple linear regre…
Resampling Procedures with Empirical Beta Copulas
Anna Kiriliouk, Johan Segers, Hideatsu Tsukahara
The empirical beta copula is a simple but effective smoother of the empirical copula. Because it is a genuine copula, from which, moreover, it is particularly easy to sample, it is…
Monte Carlo integration with a growing number of control variates
François Portier, Johan Segers
It is well known that Monte Carlo integration with variance reduction by means of control variates can be implemented by the ordinary least squares estimator for the intercept in a…
Inference for heavy tailed stationary time series based on sliding blocks
Axel Bücher, Johan Segers
The block maxima method in extreme value theory consists of fitting an extreme value distribution to a sample of block maxima extracted from a time series. Traditionally, the maxim…
Multivariate generalized Pareto distributions: parametrizations, representations, and properties
Holger Rootzén, Johan Segers, Jennifer L. Wadsworth
Multivariate generalized Pareto distributions arise as the limit distributions of exceedances over multivariate thresholds of random vectors in the domain of attraction of a max-st…
Weak convergence of the weighted empirical beta copula process
Betina Berghaus, Johan Segers
The empirical copula has proved to be useful in the construction and understanding of many statistical procedures related to dependence within random vectors. The empirical beta co…
The Empirical Beta Copula
Johan Segers, Masaaki Sibuya, Hideatsu Tsukahara
Given a sample from a multivariate distribution , the uniform random variates generated independently and rearranged in the order specified by the componentwise ranks of the ori…
On the maximum likelihood estimator for the Generalized Extreme-Value distribution
Axel Bücher, Johan Segers
The vanilla method in univariate extreme-value theory consists of fitting the three-parameter Generalized Extreme-Value (GEV) distribution to a sample of block maxima. Despite clai…
Maximum likelihood estimation for the Fréchet distribution based on block maxima extracted from a time series
Axel Bücher, Johan Segers
The block maxima method in extreme-value analysis proceeds by fitting an extreme-value distribution to a sample of block maxima extracted from an observed stretch of a time series.…
On the weak convergence of the empirical conditional copula under a simplifying assumption
François Portier, Johan Segers
When the copula of the conditional distribution of two random variables given a covariate does not depend on the value of the covariate, two conflicting intuitions arise about the…
Extreme value copula estimation based on block maxima of a multivariate stationary time series
Axel Bücher, Johan Segers
The core of the classical block maxima method consists of fitting an extreme value distribution to a sample of maxima over blocks extracted from an underlying series. In asymptotic…
When uniform weak convergence fails: Empirical processes for dependence functions and residuals via epi- and hypographs
Axel Bücher, Johan Segers, Stanislav Volgushev
In the past decades, weak convergence theory for stochastic processes has become a standard tool for analyzing the asymptotic properties of various statistics. Routinely, weak conv…
Detecting changes in cross-sectional dependence in multivariate time series
Axel Bücher, Ivan Kojadinovic, Tom Rohmer +1
Classical and more recent tests for detecting distributional changes in multivariate time series often lack power against alternatives that involve changes in the cross-sectional d…
Asymptotics of empirical copula processes under non-restrictive smoothness assumptions
Johan Segers
Weak convergence of the empirical copula process is shown to hold under the assumption that the first-order partial derivatives of the copula exist and are continuous on certain su…
Tails of correlation mixtures of elliptical copulas
Hans Manner, Johan Segers
Correlation mixtures of elliptical copulas arise when the correlation parameter is driven itself by a latent random process. For such copulas, both penultimate and asymptotic tail…
Extreme-Value Copulas
Gordon Gudendorf, Johan Segers
Being the limits of copulas of componentwise maxima in independent random samples, extreme-value copulas can be considered to provide appropriate models for the dependence structur…
Nonparametric Bayesian Inference on Bivariate Extremes
Simon Guillotte, Francois Perron, Johan Segers
The tail of a bivariate distribution function in the domain of attraction of a bivariate extreme-value distribution may be approximated by the one of its extreme-value attractor. T…