Likelihood-Based Inference for Max-Stable Processes

The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes However, their application is complicated due to the unavailability of the multivariate density function and so likehhood-based methods remain far from providing a complete and flexible framework kit inference In this article we develop inferentially practical likehhood-based methods for fitting max-stable processes derived from a composite-likehhood approach The procedure is sufficiently reliable and versatile to permit the simultaneous modeling of marginal and dependence parameters in the spatial context at a moderate computational cost The utility of this methodology is examined via simulation. and illustrated by the analysts of United States precipitation extremes


Published in:
Journal of the American Statistical Association, 105, 489, 263-277
Year:
2010
Publisher:
American Statistical Association
ISSN:
1537-274X
Keywords:
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 Record created 2010-09-16, last modified 2018-03-17

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