52 citations · 67 across the 6 of their papers we have counts for
7 papers
Causal Inference for Heterogeneous Extreme Quantiles with Heavy-Tailed Outcomes
Xiaorui Wang, Juan-Juan Cai, Huixia Judy Wang +2
We propose a framework for estimating conditional extreme quantile treatment effects (CEQTEs) in observational studies with heavy-tailed outcomes. Our procedure first estimates int…
Gradient boosting for extreme quantile regression
Jasper Velthoen, Clément Dombry, Juan-Juan Cai +1
Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the…
Interpretable random forest models through forward variable selection
Jasper Velthoen, Juan-Juan Cai, Geurt Jongbloed
Random forest is a popular prediction approach for handling high dimensional covariates. However, it often becomes infeasible to interpret the obtained high dimensional and non-par…
Parametric and non-parametric estimation of extreme earthquake event: the joint tail inference for mainshocks and aftershocks
Juan-Juan Cai, Phyllis Wan, Gamze Ozel
In an earthquake event, the combination of a strong mainshock and damaging aftershocks is often the cause of severe structural damages and/or high death tolls. The objective of thi…
Improving precipitation forecasts using extreme quantile regression
Jasper Velthoen, Juan-Juan Cai, Geurt Jongbloed +1
Aiming to estimate extreme precipitation forecast quantiles, we propose a nonparametric regression model that features a constant extreme value index. Using local linear quantile r…
Estimation of the marginal expected shortfall under asymptotic independence
Juan-Juan Cai, Eni Musta
We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positive associated, which is modeled by the so-cal…