Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84387
Title: Modeling Spatial Extremes via Ensemble-of-Trees of Pairwise Copulas
Authors: Yu, Hang
Uy, Wayne Isaac T.
Dauwels, Justin
Keywords: Graphical Models
Computational Modeling
Issue Date: 2017
Source: Yu, H., Uy, W. I. T., & Dauwels, J. (2017). Modeling Spatial Extremes via Ensemble-of-Trees of Pairwise Copulas. IEEE Transactions on Signal Processing, 65(3), 571-586.
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: Assessing the risk of extreme events in a spatial domain, such as hurricanes, floods, and droughts, presents a unique significance in practice. Unfortunately, the existing extreme-value statistical models are typically not feasible for practical large-scale problems. Graphical models, on the other hand, are capable of handling sizable number of variables, but have yet to be explored in the realm of extreme-value analysis. To bridge the gap, an extreme-value graphical model is introduced in this paper, i.e., an ensemble-of-trees of pairwise copulas (ETPC). In the proposed graphical model, extreme-value marginal distributions are stitched together by means of a pairwise copulas, which in turn are the building blocks of the ensemble of trees. Novel linear-complexity stochastic gradient-based algorithms are then developed for learning the ETPC model and inferring missing data. As a result, the ETPC model is applicable to extreme-value problems with thousands of variables. It can be proven that, under mild conditions, the ETPC model exhibits the favorable property of tail-dependence between an arbitrary pair of sites (variables); consequently, the model is able to reliably capture statistical dependence between extreme values at different sites. Experimental results for both synthetic and real data demonstrate the advantages of the ETPC model in modeling fitting, imputation, and computational efficiency.
URI: https://hdl.handle.net/10356/84387
http://hdl.handle.net/10220/43582
ISSN: 1053-587X
DOI: 10.1109/TSP.2016.2614485
Schools: School of Electrical and Electronic Engineering 
Rights: © 2017 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.2016.2614485].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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