Spatial Hierarchical Models for Extremes: Modeling Both Climate and Weather Effects
Dan
Cooley

Colorado State University

Thursday, February 11, 2010 - 3:30pm

Weather data are characterized by two types of spatial effects. Climate effects occur on a regional scale and characterize how climate varies by location. In terms of a statistical model, one can view climate effects as how the marginal distribution varies by location. Weather effects occur on a more local scale and characterize how different locations can be jointly affected by individual storms. One can think of weather effects as characterizing the joint behavior. We aim to characterize both the climate and weather effects of extreme weather, in particular extreme precipitation. Recently there have been several studies which constructed hierarchical models for spatial extreme data. We extend this line of modeling by employing a max-stable random process at the data level of the hierarchy, thereby accounting for the weather spatial effects which had often been ignored. Because the known max-stable process models can be written in closed form only for the bivariate case, we employ composite likelihood methods to implement them in our hierarchical model. Appropriate uncertainty estimates are obtained via an information sandwich approach. This is joint work with Mathieu Ribatet, EPFL, Switzerland.