242 citations · 778 across the 62 of their papers we have counts for
26 papers · 1 filter
Statistical Inference for Hüsler-Reiss Graphical Models Through Matrix Completions
Manuel Hentschel, Sebastian Engelke, Johan Segers
The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler-Reiss distribution is a versatile model for this extre…
Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments
Johan Segers, Stefka Asenova
Graphical models with heavy-tailed factors can be used to model extremal dependence or causality between extreme events. In a Bayesian network, variables are recursively defined in…
Modelling multivariate extreme value distributions via Markov trees
Shuang Hu, Zuoxiang Peng, Johan Segers
Multivariate extreme value distributions are a common choice for modelling multivariate extremes. In high dimensions, however, the construction of flexible and parsimonious models…
Extremes of Markov random fields on block graphs: max-stable limits and structured Hüsler-Reiss distributions
Stefka Asenova, Johan Segers
We study the joint occurrence of large values of a Markov random field or undirected graphical model associated to a block graph. On such graphs, containing trees as special cases,…
Multivariate goodness-of-Fit tests based on Wasserstein distance
Marc Hallin, Gilles Mordant, Johan Segers
Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple and composite null hypotheses involving general multivariate distributions. For group fami…
Inference on extremal dependence in the domain of attraction of a structured Hüsler-Reiss distribution motivated by a Markov tree with latent variables
Stefka Asenova, Gildas Mazo, Johan Segers
A Markov tree is a probabilistic graphical model for a random vector indexed by the nodes of an undirected tree encoding conditional independence relations between variables. One p…
Identifying groups of variables with the potential of being large simultaneously
Maël Chiapino, Anne Sabourin, Johan Segers
Identifying groups of variables that may be large simultaneously amounts to finding out which joint tail dependence coefficients of a multivariate distribution are positive. The as…
An estimator of the stable tail dependence function based on the empirical beta copula
Anna Kiriliouk, Johan Segers, Laleh Tafakori
The replacement of indicator functions by integrated beta kernels in the definition of the empirical stable tail dependence function is shown to produce a smoothed version of the l…
Bayesian model averaging over tree-based dependence structures for multivariate extremes
Sabrina Vettori, Raphaël Huser, Johan Segers +1
Describing the complex dependence structure of extreme phenomena is particularly challenging. To tackle this issue we develop a novel statistical algorithm that describes extremal…
Peaks over thresholds modelling with multivariate generalized Pareto distributions
Anna Kiriliouk, Holger Rootzén, Johan Segers +1
When assessing the impact of extreme events, it is often not just a single component, but the combined behaviour of several components which is important. Statistical modelling usi…
Inference on the tail process with application to financial time series modelling
R. A. Davis, H. Drees, J. Segers +1
To draw inference on serial extremal dependence within heavy-tailed Markov chains, Drees, Segers and Warchoł [Extremes (2015) 18, 369--402] proposed nonparametric estimators of the…
A continuous updating weighted least squares estimator of tail dependence in high dimensions
John H. J. Einmahl, Anna Kiriliouk, Johan Segers
Likelihood-based procedures are a common way to estimate tail dependence parameters. They are not applicable, however, in non-differentiable models such as those arising from recen…
Max-factor individual risk models with application to credit portfolios
Michel Denuit, Anna Kiriliouk, Johan Segers
Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence…
Nonparametric estimation of extremal dependence
Anna Kiriliouk, Johan Segers, Michal Warchol
There is an increasing interest to understand the dependence structure of a random vector not only in the center of its distribution but also in the tails. Extreme-value theory tac…
On the asymptotic distribution of the mean absolute deviation about the mean
Johan Segers
The mean absolute deviation about the mean is an alternative to the standard deviation for measuring dispersion in a sample or in a population. For stationary, ergodic time series…
Statistics for Tail Processes of Markov Chains
Holger Drees, Johan Segers, Michał Warchoł
At high levels, the asymptotic distribution of a stationary, regularly varying Markov chain is conveniently given by its tail process. The latter takes the form of a geometric rand…
Hybrid Copula Estimators
Johan Segers
An extension of the empirical copula is considered by combining an estimator of a multivariate cumulative distribution function with estimators of the marginal cumulative distribut…
An M-estimator of spatial tail dependence
John Einmahl, Anna Kiriliouk, Andrea Krajina +1
Tail dependence models for distributions attracted to a max-stable law are fitted using observations above a high threshold. To cope with spatial, high-dimensional data, a rank-bas…
Semiparametric Gaussian copula models: Geometry and efficient rank-based estimation
Johan Segers, Ramon van den Akker, Bas J. M. Werker
We propose, for multivariate Gaussian copula models with unknown margins and structured correlation matrices, a rank-based, semiparametrically efficient estimator for the Euclidean…
Nonparametric estimation of the tree structure of a nested Archimedean copula
Johan Segers, Nathan Uyttendaele
One of the features inherent in nested Archimedean copulas, also called hierarchical Archimedean copulas, is their rooted tree structure. A nonparametric, rank-based method to esti…
Nonparametric Inference for Max-Stable Dependence
Johan Segers
Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378].
A Euclidean likelihood estimator for bivariate tail dependence
Miguel de Carvalho, Boris Oumow, Johan Segers +1
The spectral measure plays a key role in the statistical modeling of multivariate extremes. Estimation of the spectral measure is a complex issue, given the need to obey a certain…
Nonparametric estimation of pair-copula constructions with the empirical pair-copula
Ingrid Hobaek Haff, Johan Segers
A pair-copula construction is a decomposition of a multivariate copula into a structured system, called regular vine, of bivariate copulae or pair-copulae. The standard practice is…
Measuring Association between Random Vectors
Oliver Grothe, Friedrich Schmid, Julius Schnieders +1
This paper suggests five measures of association between two random vectors X = (X_1, ..., X_p) and Y = (Y_1, ..., Y_q). They are copula based and therefore invariant with respect…