14 citations · 29 across the 12 of their papers we have counts for
13 papers
Log-Laplace Nuggets for Fully Bayesian Fitting of Spatial Extremes Models to Threshold Exceedances
Muyang Shi, Likun Zhang, Benjamin A. Shaby
Flexible random scale-mixture models provide a framework for capturing a broad range of extremal dependence structures. However, likelihood-based inference under the peaks-over-thr…
Covariance-Driven Regression Trees: Reducing Overfitting in CART
Likun Zhang, Wei Ma
Decision trees are powerful machine learning algorithms, widely used in fields such as economics and medicine for their simplicity and interpretability. However, decision trees suc…
Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders
Xiaoyu Ma, Likun Zhang, Christopher K. Wikle
Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken unde…
Modeling high and low extremes with a novel dynamic spatio-temporal model
Myungsoo Yoo, Likun Zhang, Christopher K. Wikle +1
Extreme environmental events such as severe storms, drought, heat waves, flash floods, and abrupt species collapse have become more prevalent in the earth-atmosphere dynamic system…
Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)
Likun Zhang, Kiran Bhaganagar, Christopher K. Wikle
Turbulent flow fields are characterized by extreme events that are statistically intermittent and carry a significant amount of energy and physical importance. To emulate these flo…
Spatial scale-aware tail dependence modeling for high-dimensional spatial extremes
Muyang Shi, Likun Zhang, Mark D. Risser +1
Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence clas…
Data-driven upper bounds and event attribution for unprecedented heatwaves
Mark D. Risser, Likun Zhang, Michael F. Wehner
The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case…
Interaction tests with covariate-adaptive randomization
Likun Zhang, Wei Ma
Treatment-covariate interaction tests are commonly applied by researchers to examine whether the treatment effect varies across patient subgroups defined by baseline characteristic…
Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave
Likun Zhang, Mark D. Risser, Michael F. Wehner +1
In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causin…
Flexible and efficient emulation of spatial extremes processes via variational autoencoders
Likun Zhang, Xiaoyu Ma, Christopher K. Wikle +1
Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit app…
Asymptotic posterior normality of the generalized extreme value distribution
Likun Zhang, Benjamin A. Shaby
The univariate generalized extreme value (GEV) distribution is the most commonly used tool for analyzing the properties of rare events. The ever greater utilization of Bayesian met…
Uniqueness and global optimality of the maximum likelihood estimator for the generalized extreme value distribution
Likun Zhang, Benjamin Shaby
The three-parameter generalized extreme value distribution arises from classical univariate extreme value theory and is in common use for analyzing the far tail of observed phenome…
Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets with Many Locations
Likun Zhang, Benjamin A. Shaby, Jennifer L. Wadsworth
Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally proh…