1 citations · 1 across the 5 of their papers we have counts for
5 papers
Quantile-based causal inference for spatio-temporal processes: Assessing the impacts of wildfires on US air quality
Zipei Geng, Jordan Richards, Raphael Huser +1
Wildfires pose an increasingly severe threat to air quality, yet quantifying their causal impact remains challenging due to unmeasured meteorological and geographic confounders. Mo…
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…
Modeling spatial tail dependence with Cauchy convolution processes
Pavel Krupskii, Raphaël Huser
We study the class of dependence models for spatial data obtained from Cauchy convolution processes based on different types of kernel functions. We show that the resulting spatial…
Factor Copula Models for Replicated Spatial Data
Pavel Krupskii, Raphael Huser, Marc G. Genton
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common fac…