| INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles |
13 |
| A continuous updating weighted least squares estimator of tail dependence in high dimensions |
5 |
| Fitting phase-type scale mixtures to heavy-tailed data and distributions |
4 |
| The tail process revisited |
4 |
| Multivariate extreme value copulas with factor and tree dependence structures |
4 |
| Multivariate peaks over thresholds models |
4 |
| An estimator of the stable tail dependence function based on the empirical beta copula |
3 |
| Asymptotics of convolution with the semi-regular-variation tail and its application to risk |
3 |
| On maximum of Gaussian random field having unique maximum point of its variance |
3 |
| Modeling extreme negative returns using marked renewal Hawkes processes |
3 |
| Threshold selection for multivariate heavy-tailed data |
3 |
| On the accuracy of Poisson approximation |
2 |
| Ridge regression estimators for the extreme value index |
2 |
| A nonparametric method for producing isolines of bivariate exceedance probabilities |
2 |
| Tail dimension reduction for extreme quantile estimation |
2 |
| Consistency of Hill estimators in a linear preferential attachment model |
2 |
| A Bayesian spatio-temporal model for precipitation extremes-STOR team contribution to the EVA2017 challenge |
1 |
| Prediction of extremal precipitation by quantile regression forests: from SNU Multiscale Team |
1 |
| Editorial: special issue on the extreme value analysis conference challenge prediction of extremal precipitation |
1 |
| Regular variation of a random length sequence of random variables and application to risk assessment |
1 |
| Is human life limited or unlimited? (A discussion of the paper by Holger Rootzen and Dmitrii Zholud) |
1 |
| Asymptotic normality of the likelihood moment estimators for a stationary linear process with heavy-tailed innovations |
1 |
| Processes of r(th) largest |
1 |
| Improving precipitation forecasts using extreme quantile regression |
1 |
| Extremal dependence of random scale constructions |
1 |
| Generalised least squares estimation of regularly varying space-time processes based on flexible observation schemes |
1 |
| Managing local dependencies in asymptotic theory for maxima of stationary random fields |
1 |
| Identifying groups of variables with the potential of being large simultaneously |
1 |
| Exceedance-based nonlinear regression of tail dependence |
1 |
| Extremes of projections of functional time series on data-driven basis systems |
1 |
| On the tail behavior of a class of multivariate conditionally heteroskedastic processes |
1 |
| Multivariate records and hitting scenarios |
1 |
| Certain bivariate distributions and random processes connected with maxima and minima |
0 |
| Coupled Continuous Time Random Maxima |
0 |
| Extremes of spherical fractional Brownian motion |
0 |
| The time of ultimate recovery in Gaussian risk model |
0 |
| Bias-corrected estimation for conditional Pareto-type distributions with random right censoring |
0 |
| Extremes of stationary random fields on a lattice |
0 |
| The tail dependograph |
0 |
| Multiple thresholds in extremal parameter estimation |
0 |
| Estimation of extremes for Weibull-tail distributions in the presence of random censoring |
0 |
| On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails |
0 |
| The largest order statistics for the inradius in an isotropic STIT tessellation |
0 |
| Improved estimation of the extreme value index using related variables |
0 |
| Prediction of catastrophes in space over time |
0 |
| Limit laws for the diameter of a set of random points from a distribution supported by a smoothly bounded set |
0 |
| On the study of extremes with dependent random right-censoring |
0 |
| Endpoint estimation for observations with normal measurement errors |
0 |
| Estimation of the expected shortfall given an extreme component under conditional extreme value model |
0 |
| The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles |
0 |
