Likelihood-based inference for max-stable processes

Statistics – Methodology

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

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 likelihood-based methods remain far from providing a complete and flexible framework for inference. In this article we develop inferentially practical, likelihood-based methods for fitting max-stable processes derived from a composite-likelihood 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 analysis of U.S. precipitation extremes.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Likelihood-based inference for max-stable processes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Likelihood-based inference for max-stable processes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Likelihood-based inference for max-stable processes will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-127902

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.