Extreme-Value Graphical Models With Multiple Covariates

To assess the risk of extreme events such as hurricanes, earthquakes, and floods, it is crucial to develop accurate extreme-value statistical models. Extreme events often display heterogeneity (i.e., nonstationarity), varying continuously with a number of covariates. Previous studies have suggested...

Full description

Saved in:
Bibliographic Details
Main Authors: Yu, Hang, Dauwels, Justin, Jonathan, Philip
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/89366
http://hdl.handle.net/10220/44846
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-89366
record_format dspace
spelling sg-ntu-dr.10356-893662020-03-07T14:02:37Z Extreme-Value Graphical Models With Multiple Covariates Yu, Hang Dauwels, Justin Jonathan, Philip School of Electrical and Electronic Engineering Covariates Extreme Events Modeling To assess the risk of extreme events such as hurricanes, earthquakes, and floods, it is crucial to develop accurate extreme-value statistical models. Extreme events often display heterogeneity (i.e., nonstationarity), varying continuously with a number of covariates. Previous studies have suggested that models considering covariate effects lead to reliable estimates of extreme events distributions. In this paper, we develop a novel statistical model to incorporate the effects of multiple covariates. Specifically, we analyze as an example the extreme sea states in the Gulf of Mexico, where the distribution of extreme wave heights changes systematically with location and storm direction. In the proposed model, the block maximum at each location and sector of wind direction are assumed to follow the Generalized Extreme Value (GEV) distribution. The GEV parameters are coupled across the spatio-directional domain through a graphical model, in particular, a three-dimensional (3D) thin-membrane model. Efficient learning and inference algorithms are developed based on the special characteristics of the thin-membrane model. We further show how to extend the model to incorporate an arbitrary number of covariates in a straightforward manner. Numerical results for both synthetic and real data indicate that the proposed model can accurately describe marginal behaviors of extreme events. MOE (Min. of Education, S’pore) Accepted version 2018-05-21T08:05:44Z 2019-12-06T17:23:56Z 2018-05-21T08:05:44Z 2019-12-06T17:23:56Z 2014 Journal Article Yu, H., Dauwels, J., & Jonathan, P. (2014). Extreme-Value Graphical Models With Multiple Covariates. IEEE Transactions on Signal Processing, 62(21), 5734-5747. 1053-587X https://hdl.handle.net/10356/89366 http://hdl.handle.net/10220/44846 10.1109/TSP.2014.2358955 en IEEE Transactions on Signal Processing © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSP.2014.2358955]. 14 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Covariates
Extreme Events Modeling
spellingShingle Covariates
Extreme Events Modeling
Yu, Hang
Dauwels, Justin
Jonathan, Philip
Extreme-Value Graphical Models With Multiple Covariates
description To assess the risk of extreme events such as hurricanes, earthquakes, and floods, it is crucial to develop accurate extreme-value statistical models. Extreme events often display heterogeneity (i.e., nonstationarity), varying continuously with a number of covariates. Previous studies have suggested that models considering covariate effects lead to reliable estimates of extreme events distributions. In this paper, we develop a novel statistical model to incorporate the effects of multiple covariates. Specifically, we analyze as an example the extreme sea states in the Gulf of Mexico, where the distribution of extreme wave heights changes systematically with location and storm direction. In the proposed model, the block maximum at each location and sector of wind direction are assumed to follow the Generalized Extreme Value (GEV) distribution. The GEV parameters are coupled across the spatio-directional domain through a graphical model, in particular, a three-dimensional (3D) thin-membrane model. Efficient learning and inference algorithms are developed based on the special characteristics of the thin-membrane model. We further show how to extend the model to incorporate an arbitrary number of covariates in a straightforward manner. Numerical results for both synthetic and real data indicate that the proposed model can accurately describe marginal behaviors of extreme events.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Yu, Hang
Dauwels, Justin
Jonathan, Philip
format Article
author Yu, Hang
Dauwels, Justin
Jonathan, Philip
author_sort Yu, Hang
title Extreme-Value Graphical Models With Multiple Covariates
title_short Extreme-Value Graphical Models With Multiple Covariates
title_full Extreme-Value Graphical Models With Multiple Covariates
title_fullStr Extreme-Value Graphical Models With Multiple Covariates
title_full_unstemmed Extreme-Value Graphical Models With Multiple Covariates
title_sort extreme-value graphical models with multiple covariates
publishDate 2018
url https://hdl.handle.net/10356/89366
http://hdl.handle.net/10220/44846
_version_ 1681043052481740800