Publications by Jonathan Koh Boon Han

Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. Here, we develop a joint model for the occurrence intensity and t ...

2023

Gradient boosting with extreme-value theory for wildfire prediction

Jonathan Koh Boon Han

This paper details the approach of the team Kohrrelation in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a machine lear ...

2023

Spatiotemporal modelling of extreme wildfires and severe thunderstorm environments

Jonathan Koh Boon Han

Environmental extreme events can have devastating impacts on society when they interact with vulnerable human and natural systems. Such events can result from natural causes, like phenomena related to the El Ni~no-Southern Oscillation or decadal/multi-dec ...

EPFL2022

Trends in the Extremes of Environments Associated with Severe US Thunderstorms

Anthony Christopher Davison, Erwan Fabrice Koch, Jonathan Koh Boon Han

Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of PROD - (CAPE)(1/2) x SRH have been ...

AMER METEOROLOGICAL SOC2021

Comment: Models Are Approximations!

Anthony Christopher Davison, Erwan Fabrice Koch, Jonathan Koh Boon Han

This discussion focuses on areas of disagreement with the papers, particularly the target of inference and the case for using the robust 'sandwich' variance estimator in the presence of moderate mis-specification. We also suggest that existing procedures m ...

INST MATHEMATICAL STATISTICS2019

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Vlad Popovici, Jun Luo, Darlene Goldstein, Mauro Delorenzi, Lu Meng, Li Li, Li Zhang, Wen Luo, Li Guo, Jonathan Koh Boon Han, Wei Wang, Rui Jiang, Jie Liu, Ying Liu, Chenxu Zhao, Rong Chen

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to gene ...

Nature Publishing Group2010