Recent wildfires in Australia have led to considerable economic loss and...
Wildfires pose a severe threat to the ecosystem and economy, and risk
as...
Moving average processes driven by exponential-tailed Lévy noise are
imp...
We develop a methodology for modelling and simulating high-dimensional
s...
Inference for spatial extremal dependence models can be computationally
...
Brain connectivity reflects how different regions of the brain interact
...
In environmental science applications, extreme events frequently exhibit...
Current methods for clustering nodes over time in a brain network are
de...
Extreme precipitation events with large spatial extents may have more se...
Extreme wildfires continue to be a significant cause of human death and
...
We propose a novel extremal dependence measure called the partial
tail-c...
Statistical modeling of a nonstationary spatial extremal dependence stru...
A successful model for high-dimensional spatial extremes should, in
prin...
Neural networks have recently shown promise for likelihood-free inferenc...
Risk management in many environmental settings requires an understanding...
To study the neurophysiological basis of attention deficit hyperactivity...
We develop a novel multi-factor copula model for multivariate spatial
ex...
To accurately quantify landslide hazard in a region of Turkey, we develo...
Max-stable processes are the most popular models for high-impact spatial...
This paper describes the methodology used by the team RedSea in the data...
Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge w...
Various natural phenomena exhibit spatial extremal dependence at short
d...
In this work, we develop a constructive modeling framework for extreme
t...
In this chapter, we show how to efficiently model high-dimensional extre...
The generalised extreme value (GEV) distribution is a three parameter fa...
Classical models for multivariate or spatial extremes are mainly based u...
Max-infinitely divisible (max-id) processes play a central role in
extre...
We study the class of models for spatial data obtained from Cauchy
convo...
Correlated binary response data with covariates are ubiquitous in
longit...
Epilepsy is a chronic neurological disorder affecting more than 50 milli...
The classical modeling of spatial extremes relies on asymptotic models (...
Statistical models for landslide hazard enable mapping of risk factors a...
The modeling of spatio-temporal trends in temperature extremes can help
...
In this work, we estimate extreme sea surface temperature (SST) hotspots...
In this work, we focus on estimating sea surface temperature (SST) hotsp...
We develop new flexible univariate models for light-tailed and heavy-tai...
Landslides are nearly ubiquitous phenomena and pose severe threats to pe...
Large, non-stationary spatio-temporal data are ubiquitous in modern
stat...
With modern high-dimensional data, complex statistical models are necess...
Extreme floods cause casualties, widespread property damage, and damage ...
Since the inception of Bitcoin in 2008, cryptocurrencies have played an
...
Renewable sources of energy such as wind power have become a sustainable...
In this paper, we investigate earthquake-induced landslides using a
geos...
Understanding the spatial extent of extreme precipitation is necessary f...
Capturing the potentially strong dependence among the peak concentration...
This work has been motivated by the challenge of the 2017 conference on
...
Extreme-value theory for stochastic processes has motivated the statisti...
In order to model the complex non-stationary dependence structure of
pre...