Modeling extreme events in spatial domain by copula graphical models

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Modeling extreme events in spatial domain by copula graphical models. / Yu, H.; Choo, Z.; Uy, W.I.T. et al.
2012 15th International Conference on Information Fusion. IEEE, 2012. p. 1761-1768.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Yu, H, Choo, Z, Uy, WIT, Dauwels, J & Jonathan, P 2012, Modeling extreme events in spatial domain by copula graphical models. in 2012 15th International Conference on Information Fusion. IEEE, pp. 1761-1768. <https://ieeexplore.ieee.org/document/6290516>

APA

Yu, H., Choo, Z., Uy, W. I. T., Dauwels, J., & Jonathan, P. (2012). Modeling extreme events in spatial domain by copula graphical models. In 2012 15th International Conference on Information Fusion (pp. 1761-1768). IEEE. https://ieeexplore.ieee.org/document/6290516

Vancouver

Yu H, Choo Z, Uy WIT, Dauwels J, Jonathan P. Modeling extreme events in spatial domain by copula graphical models. In 2012 15th International Conference on Information Fusion. IEEE. 2012. p. 1761-1768

Author

Yu, H. ; Choo, Z. ; Uy, W.I.T. et al. / Modeling extreme events in spatial domain by copula graphical models. 2012 15th International Conference on Information Fusion. IEEE, 2012. pp. 1761-1768

Bibtex

@inproceedings{5d8c4882669846cf82d8295b96a438e4,

title = "Modeling extreme events in spatial domain by copula graphical models",

abstract = "We propose a new statistical model that captures the conditional dependence among extreme events in a spatial domain. This model may for instance be used to describe catastrophic events such as earthquakes, floods, or hurricanes in certain regions, and in particular to predict extreme values at unmonitored sites. The proposed model is derived as follows. The block maxima at each location are assumed to follow a Generalized Extreme Value (GEV) distribution. Spatial dependence is modeled in two complementary ways. The GEV parameters are coupled through a thin-membrane model, a specific type of Gaussian graphical model often used as smoothness prior. The extreme events, on the other hand, are coupled through a copula Gaussian graphical model with the precision matrix corresponding to a (generalized) thin-membrane model. We then derive inference and interpolation algorithms for the proposed model. The approach is validated on synthetic data as well as real data related to hurricanes in the Gulf of Mexico. Numerical results suggest that it can accurately describe extreme events in spatial domain, and can reliably interpolate extreme values at arbitrary sites. {\textcopyright} 2012 ISIF (Intl Society of Information Fusi).",

keywords = "Block maxima, Catastrophic event, Conditional dependence, Extreme events, Extreme value, Gaussians, Generalized extreme value, GraphicaL model, Gulf of Mexico, Interpolation algorithms, Numerical results, Precision matrix, Spatial dependence, Spatial domains, Statistical models, Synthetic data, Graphic methods, Hurricanes, Inference engines, Information fusion, Speech recognition",

author = "H. Yu and Z. Choo and W.I.T. Uy and J. Dauwels and P. Jonathan",

year = "2012",

language = "English",

isbn = "9781467304177",

pages = "1761--1768",

booktitle = "2012 15th International Conference on Information Fusion",

publisher = "IEEE",

}

RIS

TY - GEN

T1 - Modeling extreme events in spatial domain by copula graphical models

AU - Yu, H.

AU - Choo, Z.

AU - Uy, W.I.T.

AU - Dauwels, J.

AU - Jonathan, P.

PY - 2012

Y1 - 2012

N2 - We propose a new statistical model that captures the conditional dependence among extreme events in a spatial domain. This model may for instance be used to describe catastrophic events such as earthquakes, floods, or hurricanes in certain regions, and in particular to predict extreme values at unmonitored sites. The proposed model is derived as follows. The block maxima at each location are assumed to follow a Generalized Extreme Value (GEV) distribution. Spatial dependence is modeled in two complementary ways. The GEV parameters are coupled through a thin-membrane model, a specific type of Gaussian graphical model often used as smoothness prior. The extreme events, on the other hand, are coupled through a copula Gaussian graphical model with the precision matrix corresponding to a (generalized) thin-membrane model. We then derive inference and interpolation algorithms for the proposed model. The approach is validated on synthetic data as well as real data related to hurricanes in the Gulf of Mexico. Numerical results suggest that it can accurately describe extreme events in spatial domain, and can reliably interpolate extreme values at arbitrary sites. © 2012 ISIF (Intl Society of Information Fusi).

AB - We propose a new statistical model that captures the conditional dependence among extreme events in a spatial domain. This model may for instance be used to describe catastrophic events such as earthquakes, floods, or hurricanes in certain regions, and in particular to predict extreme values at unmonitored sites. The proposed model is derived as follows. The block maxima at each location are assumed to follow a Generalized Extreme Value (GEV) distribution. Spatial dependence is modeled in two complementary ways. The GEV parameters are coupled through a thin-membrane model, a specific type of Gaussian graphical model often used as smoothness prior. The extreme events, on the other hand, are coupled through a copula Gaussian graphical model with the precision matrix corresponding to a (generalized) thin-membrane model. We then derive inference and interpolation algorithms for the proposed model. The approach is validated on synthetic data as well as real data related to hurricanes in the Gulf of Mexico. Numerical results suggest that it can accurately describe extreme events in spatial domain, and can reliably interpolate extreme values at arbitrary sites. © 2012 ISIF (Intl Society of Information Fusi).

KW - Block maxima

KW - Catastrophic event

KW - Conditional dependence

KW - Extreme events

KW - Extreme value

KW - Gaussians

KW - Generalized extreme value

KW - GraphicaL model

KW - Gulf of Mexico

KW - Interpolation algorithms

KW - Numerical results

KW - Precision matrix

KW - Spatial dependence

KW - Spatial domains

KW - Statistical models

KW - Synthetic data

KW - Graphic methods

KW - Hurricanes

KW - Inference engines

KW - Information fusion

KW - Speech recognition

M3 - Conference contribution/Paper

SN - 9781467304177

SP - 1761

EP - 1768

BT - 2012 15th International Conference on Information Fusion

PB - IEEE

ER -

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