Advances in statistical modeling of spatial extremes
R Huser, JL Wadsworth - Wiley Interdisciplinary Reviews …, 2022 - Wiley Online Library
The classical modeling of spatial extremes relies on asymptotic models (ie, max‐stable or r‐
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …
Sparse structures for multivariate extremes
Extreme value statistics provides accurate estimates for the small occurrence probabilities of
rare events. While theory and statistical tools for univariate extremes are well developed …
rare events. While theory and statistical tools for univariate extremes are well developed …
Graphical models for extremes
Conditional independence, graphical models and sparsity are key notions for parsimonious
statistical models and for understanding the structural relationships in the data. The theory of …
statistical models and for understanding the structural relationships in the data. The theory of …
A multivariate method for evaluating safety from conflict extremes in real time
C Fu, T Sayed - Analytic methods in accident research, 2022 - Elsevier
Several studies have advocated the use of extreme value theory (EVT) traffic conflict models
for real-time crash risk prediction using real-time safety indices such as the risk of crash (RC) …
for real-time crash risk prediction using real-time safety indices such as the risk of crash (RC) …
Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Y Boulaguiem, J Zscheischler, E Vignotto… - Environmental Data …, 2022 - cambridge.org
Modeling dependencies between climate extremes is important for climate risk assessment,
for instance when allocating emergency management funds. In statistics, multivariate …
for instance when allocating emergency management funds. In statistics, multivariate …
Full likelihood inference for max‐stable data
We show how to perform full likelihood inference for max‐stable multivariate distributions or
processes based on a stochastic expectation–maximization algorithm, which combines …
processes based on a stochastic expectation–maximization algorithm, which combines …
Vecchia likelihood approximation for accurate and fast inference with intractable spatial max-stable models
Max-stable processes are the most popular models for high-impact spatial extreme events,
as they arise as the only possible limits of spatially-indexed block maxima. However …
as they arise as the only possible limits of spatially-indexed block maxima. However …
A hierarchical max-infinitely divisible spatial model for extreme precipitation
Understanding the spatial extent of extreme precipitation is necessary for determining flood
risk and adequately designing infrastructure (eg, stormwater pipes) to withstand such …
risk and adequately designing infrastructure (eg, stormwater pipes) to withstand such …
Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network
CH Oh, JI Lee - Reliability Engineering & System Safety, 2020 - Elsevier
In a reactor, various reactor instruments inform the operator of changes in the operating
conditions. However, until now, the identification of the reactor state from interpretations of …
conditions. However, until now, the identification of the reactor state from interpretations of …
Vecchia likelihood approximation for accurate and fast inference in intractable spatial extremes models
Max-stable processes are the most popular models for high-impact spatial extreme events,
as they arise as the only possible limits of spatially-indexed block maxima. However …
as they arise as the only possible limits of spatially-indexed block maxima. However …