most citedVecchia Likelihood Approximation for Accurate and Fast Inference in Intractable Spatial Extremes Models

7 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME20221 cited

Spatial modeling and future projection of extreme precipitation extents

Peng Zhong, Manuela Brunner, Thomas Opitz +1

Extreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change…

stat.ME2022

Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning

Yan Gong, Peng Zhong, Thomas Opitz +1

We propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC), in analogy to the partial correlation coefficient in classical multivariate a…

stat.ME20227 cited

Vecchia Likelihood Approximation for Accurate and Fast Inference in Intractable Spatial Extremes Models

Raphaël Huser, Michael L. Stein, Peng Zhong

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, likel…

stat.AP2022

Joint Modeling and Prediction of Massive Spatio-Temporal Wildfire Count and Burnt Area Data with the INLA-SPDE Approach

Zhongwei Zhang, Elias Krainski, Peng Zhong +2

This paper describes the methodology used by the team RedSea in the data competition organized for EVA 2021 conference. We develop a novel two-part model to jointly describe the wi…

stat.ME2020

Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude

Peng Zhong, Raphaël Huser, Thomas Opitz

The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temp…