Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called `angular dependence function'; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.
%0 Journal Article
%1 Murphy-Barltrop2024
%A Murphy-Barltrop, C. J. R.
%A Wadsworth, J. L.
%A Eastoe, E. F.
%D 2024
%J Extremes
%K imported from:scadsfct
%R 10.1007/s10687-024-00490-4
%T Improving Estimation for Asymptotically Independent Bivariate Extremes via Global Estimators for the Angular Dependence Function
%U https://doi.org/10.1007/s10687-024-00490-4
%X Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called `angular dependence function'; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.
@article{Murphy-Barltrop2024,
abstract = {Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called `angular dependence function'; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.},
added-at = {2024-10-17T10:45:06.000+0200},
author = {Murphy-Barltrop, C. J. R. and Wadsworth, J. L. and Eastoe, E. F.},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2a4c5c31c17d944732312d789a75bfb1c/scads.ai},
day = 13,
doi = {10.1007/s10687-024-00490-4},
interhash = {e1f0bd4f074d17edb5e56aef2f0acc4a},
intrahash = {a4c5c31c17d944732312d789a75bfb1c},
issn = {1572-915X},
journal = {Extremes},
keywords = {imported from:scadsfct},
month = aug,
timestamp = {2024-10-17T10:45:06.000+0200},
title = {Improving Estimation for Asymptotically Independent Bivariate Extremes via Global Estimators for the Angular Dependence Function},
url = {https://doi.org/10.1007/s10687-024-00490-4},
year = 2024
}
