1 citations · 2 across the 5 of their papers we have counts for
8 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…