Premium
Sampling from a Max‐Stable Process Conditional on a Homogeneous Functional with an Application for Downscaling Climate Data
Author(s) -
Oesting Marco,
Bel Liliane,
Lantuéjoul Christian
Publication year - 2018
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12299
Subject(s) - downscaling , mathematics , conditional probability distribution , markov chain , context (archaeology) , markov chain monte carlo , convergence (economics) , sampling (signal processing) , econometrics , monte carlo method , statistics , precipitation , computer science , meteorology , geography , archaeology , filter (signal processing) , computer vision , economic growth , economics
Conditional simulation of max‐stable processes allows for the analysis of spatial extremes taking into account additional information provided by the conditions. Instead of observations at given sites as usually done, we consider a single condition given by a more general functional of the process as may occur in the context of climate models. As the problem turns out to be intractable analytically, we make use of Markov chain Monte Carlo methods to sample from the conditional distribution. Simulation studies indicate fast convergence of the Markov chains involved. In an application to precipitation data, the utility of the procedure as a tool to downscale climate data is demonstrated.