3 citations · 6 across the 12 of their papers we have counts for
4 papers · 1 filter
Max-convolution processes with random shape indicator kernels
Pavel Krupskii, Raphaël Huser
In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that this class of…
A Neural Network-Based Approach to Normality Testing for Dependent Data
Minwoo Kim, Marc G Genton, Raphael Huser +1
There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, ho…
Spatial wildfire risk modeling using mixtures of tree-based multivariate Pareto distributions
Daniela Cisneros, Arnab Hazra, Raphaël Huser
Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur Forest Fire Danger Index (FFDI) used…
Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise
Zhongwei Zhang, David Bolin, Sebastian Engelke +1
Moving average processes driven by exponential-tailed Lévy noise are important extensions of their Gaussian counterparts in order to capture deviations from Gaussianity, more flexi…