Weather data are characterized by two types of spatial effects: climate effects that occur on a regional scale and weather effects that occur on a local scale. In terms of a statistical model, one can view climate effects as how the marginal distribution varies by location and the weather effects as characterizing the joint behavior. We extend recent work in spatial hierarchical models for extremes 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.
Keywords: Precipitation; Max-stable processes
Biography: Daniel Cooley received his PhD in 2005, and was a postdoctoral researcher at the National Center for Atmospheric Research for two years. He joined the faculty of the statstics department at Colorado State University in 2007 where he has been since. His main research areas deal with spatial extremes and atmopheric applications.