Threshold selection for multivariate heavy-tailed data

DOI10.1007/s10687-018-0316-xzbMath1418.62220arXiv1707.00464OpenAlexW2962897551MaRDI QIDQ2418002

Richard A. Davis, Phyllis Wan

Publication date: 31 May 2019

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.00464

zbMATH Keywords

threshold selectiondistance covariancemultivariate regular variationheavy-tailed data

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)

Related Items

Bayesian Spatial Clustering of Extremal Behavior for Hydrological Variables ⋮ Multivariate matrix Mittag-Leffler distributions ⋮ Semiparametric bivariate modelling with flexible extremal dependence

Uses Software

• wbs

Cites Work

• Unnamed Item
• Unnamed Item
• Measuring and testing dependence by correlation of distances
• Wild binary segmentation for multiple change-point detection
• Towards estimating extremal serial dependence via the bootstrapped extremogram
• The extremogram: a correlogram for extreme events
• Sea and wind: multivariate extremes at work
• Mixing: Properties and examples
• Hidden regular variation, second order regular variation and asymptotic independence
• Applications of distance correlation to time series
• Threshold estimation based on a p-value framework in dose-response and regression settings
• A Consistent Test for Bivariate Dependence
• Heavy-Tail Phenomena
• Convergence Criteria for Multiparameter Stochastic Processes and Some Applications
• CONTINUOUS INSPECTION SCHEMES