A new representation for multivariate tail probabilities
From MaRDI portal
Publication:2435257
DOI10.3150/12-BEJ471zbMath1284.60107arXiv1312.5442OpenAlexW3099331706MaRDI QIDQ2435257
Jennifer L. Wadsworth, Jonathan A. Tawn
Publication date: 4 February 2014
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1312.5442
regular variationasymptotic independencecoefficient of tail dependencemultivariate extreme value theoryPickands' dependence function
Related Items
Linking representations for multivariate extremes via a limit set, Approximation and estimation of very small probabilities of multivariate extreme events, Inference for asymptotically independent samples of extremes, \(k\)th-order Markov extremal models for assessing heatwave risks, Extremes for a general contagion risk measure, A modeler's guide to extreme value software, A crossinggram for random fields on lattices, Exchangeable random partitions from max-infinitely-divisible distributions, Properties of extremal dependence models built on bivariate MAX-linearity, A nonparametric method for producing isolines of bivariate exceedance probabilities, Conditional excess risk measures and multivariate regular variation, Conditioned limit laws for inverted max-stable processes, Exceedance-based nonlinear regression of tail dependence
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotic normality of Hill's estimator for the exponent of regular variation
- A simple general approach to inference about the tail of a distribution
- Sea and wind: multivariate extremes at work
- Hidden regular variation, second order regular variation and asymptotic independence
- Distribution and dependence-function estimation for bivariate extreme-value distributions.
- On multivariate Gaussian tails
- Limit laws for random vectors with an extreme component
- Characterizations and examples of hidden regular variation
- Mill's ratio for multivariate normal distributions
- A New Class of Models for Bivariate Joint Tails
- Bivariate extreme value theory: Models and estimation
- Statistics for near independence in multivariate extreme values
- On asymptotic normality of the hill estimator
- A Conditional Approach for Multivariate Extreme Values (with Discussion)
- Statistics of Extremes
- Heavy-Tail Phenomena
- Hidden regular variation and the rank transform
- An asymptotic expansion for the multivariate normal distribution and Mills' ratio
- Convex functions and their applications. A contemporary approach
