Type
ArticleAuthors
Genton, Marc G.
Padoan, S. A.
Sang, H.
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionSpatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2015-02-11Online Publication Date
2015-02-11Print Publication Date
2015-03-01Permanent link to this record
http://hdl.handle.net/10754/552385
Metadata
Show full item recordAbstract
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.Citation
Multivariate max-stable spatial processes 2015, 102 (1):215 BiometrikaPublisher
Oxford University Press (OUP)Journal
BiometrikaAdditional Links
http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/asu066ae974a485f413a2113503eed53cd6c53
10.1093/biomet/asu066