A new mixture model for spatiotemporal extremes with flexible tail dependence

Music City Center 

We propose a new model and estimation framework for spatiotemporal streamflow outcomes that flexibly captures asymptotic dependence and independence in the tail of the distribution. We model streamflow using a mixture of Gaussian and max-stable spatial and temporal random variables. A censoring mechanism allows us to leverage observations in the bulk to improve modeling of the tail. As the likelihood is intractable, we develop a deep Vecchia approximation to the likelihood using neural networks to fit a flexible quantile regression model with monotonic splines. Simulations and modeling of streamflow data from the U.S. Geological Survey illustrate the feasibility and practicality of our approach.