Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89366
Title: Extreme-Value Graphical Models With Multiple Covariates
Authors: Yu, Hang
Dauwels, Justin
Jonathan, Philip
Keywords: Covariates
Extreme Events Modeling
Issue Date: 2014
Source: Yu, H., Dauwels, J., & Jonathan, P. (2014). Extreme-Value Graphical Models With Multiple Covariates. IEEE Transactions on Signal Processing, 62(21), 5734-5747.
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: 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.
URI: https://hdl.handle.net/10356/89366
http://hdl.handle.net/10220/44846
ISSN: 1053-587X
DOI: http://dx.doi.org/10.1109/TSP.2014.2358955
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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