3 citations · 5 across the 6 of their papers we have counts for
6 papers
Diagnostic Tools for Extreme Value Regression Models
Ed Mackay, Jordan Richards, Philip Jonathan
Visual and quantitative goodness-of-fit diagnostics are an important tool in the practitioner's toolbox. The need for convincing and reliable diagnostics is particularly clear when…
Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States
Ben Seiyon Lee, Reetam Majumder, Jordan Richards +2
Understanding and mapping extreme heat is critical for risk management and public health planning, particularly in regions with complex terrain and heterogeneous climate. We presen…
Generative modelling of multivariate geometric extremes using normalising flows
Lambert De Monte, Raphaël Huser, Ioannis Papastathopoulos +1
Leveraging the recently emerging geometric approach to multivariate extremes and the flexibility of normalising flows on the hypersphere, we propose a principled deep-learning-base…
Semi-parametric bulk and tail regression using spline-based neural networks
Reetam Majumder, Jordan Richards
Semi-parametric quantile regression (SPQR) is a flexible approach to density regression that learns a spline-based representation of conditional density functions using neural netw…
Deep learning joint extremes of metocean variables using the SPAR model
Ed Mackay, Callum Murphy-Barltrop, Jordan Richards +1
This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When…
Deep Learning of Multivariate Extremes via a Geometric Representation
Callum J. R. Murphy-Barltrop, Reetam Majumder, Jordan Richards
The study of geometric extremes, where extremal dependence properties are inferred from the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to…