A0386
Title: Distributed inference for extreme value analysis of large spatial datasets
Authors: Emily Hector - North Carolina State University (United States) [presenting]
Abstract: Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes that are computationally prohibitive to fit for as few as a dozen observations. We propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers computationally and statistically efficient inference. The proposed distributed approach is extended to estimate spatially varying coefficient models to deliver computationally efficient modeling of spatial variation in marginal parameters. We illustrate the flexibility of our approach through simulations and the analysis of streamflow data from the U.S. Geological Survey.
