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...
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| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/89366 http://hdl.handle.net/10220/44846 |
| _version_ | 1826123446346579968 |
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| author | Yu, Hang Dauwels, Justin Jonathan, Philip |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yu, Hang Dauwels, Justin Jonathan, Philip |
| author_sort | Yu, Hang |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:04:28Z |
| format | Journal Article |
| id | ntu-10356/89366 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:04:28Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Covariates Extreme Events Modeling Yu, Hang Dauwels, Justin Jonathan, Philip Extreme-Value Graphical Models With Multiple Covariates |
| title | 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_short | Extreme-Value Graphical Models With Multiple Covariates |
| title_sort | extreme value graphical models with multiple covariates |
| topic | Covariates Extreme Events Modeling |
| url | https://hdl.handle.net/10356/89366 http://hdl.handle.net/10220/44846 |
| work_keys_str_mv | AT yuhang extremevaluegraphicalmodelswithmultiplecovariates AT dauwelsjustin extremevaluegraphicalmodelswithmultiplecovariates AT jonathanphilip extremevaluegraphicalmodelswithmultiplecovariates |