Likelihood-free neural Bayes estimators for censored peaks-over-threshold models

Inference for spatial extremal dependence models can be computationally burdensome in moderate-to-high dimensions due to their reliance on intractable and/or censored likelihoods. Exploiting recent advances in likelihood-free inference with neural Bayes estimators (that is, neural estimators that target Bayes estimators), we develop a novel approach to construct highly efficient estimators for censored peaks-over-threshold models by encoding censoring information in the neural network architecture. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference for spatial extremes. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators for inference of popular extremal dependence models, such as max-stable, $r$-Pareto, and random scale mixture processes. We also illustrate that it is possible to train a single estimator for a general censoring level, obviating the need to retrain when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess particulate matter 2.5 microns or less in diameter (PM2.5) concentration over the whole of Saudi Arabia.