A0480
Title: Neural Bayes estimators for fast and efficient inference with spatial peaks-over-threshold models
Authors: Jordan Richards - King Abdullah University of Science and Technology (Saudi Arabia) [presenting]
Matthew Sainsbury-Dale - University of Wollongong (Australia)
Andrew Zammit Mangion - University of Wollongong (Australia)
Raphael Huser - King Abdullah University of Science and Technology (Saudi Arabia)
Abstract: Likelihood-based inference for spatial extremal dependence models is often infeasible in moderate or high dimensions due to an intractable likelihood function and/or the need for computationally-expensive censoring to reduce estimation bias. Neural Bayes estimators are a promising recent approach to inference that use neural networks to transform data into parameter estimates. They are likelihood free, inherit the optimality properties of Bayes estimators, and are substantially faster than classical methods. Neural Bayes estimators are adapted for peaks-over-threshold dependence models; in particular, a methodology is developed for coping with the computational challenges often encountered when modelling spatial extremes (e.g., censoring). It is demonstrated substantial improvements in computational and statistical efficiency relative to conventional likelihood-based approaches using popular extremal dependence models, including max-stable, and r-Pareto, processes and random scale mixture models.
