Modeling Spatial Processes with Unknown Extremal Dependence Class
Name:
Modeling Spatial Processes with Unknown Extremal Dependence Class.pdf
Size:
1.322Mb
Format:
PDF
Description:
Published version
Name:
Journal_Code.zip
Size:
38.84Kb
Format:
application/zip
Description:
Supplemental files
Type
ArticleAuthors
Huser, Raphaël
Wadsworth, Jennifer L.
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-06-28Preprint Posting Date
2017-03-17Online Publication Date
2018-06-28Print Publication Date
2019-01-02Permanent link to this record
http://hdl.handle.net/10754/626518
Metadata
Show full item recordAbstract
Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, that is, the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure. Supplementary materials for this article are available online.Citation
Huser R, Wadsworth JL (2018) Modeling Spatial Processes with Unknown Extremal Dependence Class. Journal of the American Statistical Association: 1–11. Available: http://dx.doi.org/10.1080/01621459.2017.1411813.Sponsors
The authors thank Philip Jonathan of Shell Research for the wave height data analyzed in Section 4.1, and Raphaël de Fondeville for helpful discussions and code for multivariate Gaussian computation.Publisher
Informa UK LimitedarXiv
1703.06031Additional Links
https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1411813Relations
Is Supplemented By:- [Dataset]
Huser, R., & Wadsworth, J. L. (2018). Modeling Spatial Processes with Unknown Extremal Dependence Class [Data set]. Taylor & Francis. https://doi.org/10.6084/M9.FIGSHARE.5787555.V4. DOI: 10.6084/m9.figshare.5787555.v4 Handle: 10754/664225
ae974a485f413a2113503eed53cd6c53
10.1080/01621459.2017.1411813
Scopus Count
Collections
Articles
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.