A Spatio-Temporal Dynamic Model Accommodating Space and Time-Varying Extremes

Oregon Convention Center 

Many climatological and environmental dynamic processes exhibit Gaussian behavior. However, for various reasons, it is often the case that some clusters of locations in space and periods of time exhibit lower or upper tail extremes. We propose a bulk and tail spatio-temporal dynamic model (DSTM) that can accommodate such behavior, with either Gaussian or heavy tail behavior varying across the spatial domain and across time. This is accomplished by a regime switching model with stable and Gaussian distributions on the conditional innovation process. The non-stationarity is facilitated by a reduced rank basis function representation and estimated efficiently in a Bayesian inferential paradigm. The model is demonstrated through simulation and application to environmental data.