Title: Local likelihood estimation of complex tail dependence structures in high dimensions applied to precipitation extremes
Authors: Raphael Huser - King Abdullah University of Science and Technology (Saudi Arabia)
Daniela Castro-Camilo - University of Glasgow (United Kingdom) [presenting]
Abstract: To model the non-stationary dependence structure of precipitation extremes over the entire US, we propose a flexible local approach based on factor copula models. Our sub-asymptotic spatial model has a non-trivial tail dependence structure with a weakening dependence strength as events become more extreme, a feature commonly observed with precipitation data but not accounted for in asymptotic extreme-value models. To estimate the local joint tail behavior, we fit the proposed model in small regional neighborhoods to high threshold exceedances, under the assumption of local stationarity. This allows us to gain in flexibility while making inference for such a large and complex dataset feasible. Adopting a local censored likelihood approach, inference is made on a fine spatial grid, and local estimation is performed taking advantage of large distributed computing resources. An extensive simulation study shows that our approach is able to adequately capture complex dependencies, and our study of US winter precipitation data reveals interesting differences in local tail structures over space, which has important implications on regional risk assessment of extreme precipitation events.