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Name: Clement Dombry

Talk Title: Gradient boosting for extreme quantile regression

Abstract: Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical methods such as quantile random forests perform poorly in such cases since data in the tail region are too scarce. Extreme value theory moti-vates to approximate the conditional distribution above a high threshold by a general- ized Pareto distribution with covariate dependent parameters. This model allows for extrapolation beyond the range of observed values and estimation of conditional ex-treme quantiles. We propose a gradient boosting procedure to estimate a conditional generalized Pareto distribution by minimizing its deviance. Cross-validation is used for the choice of tuning parameters such as the number of trees and the tree depths. We discuss diagnostic plots such as variable importance and partial dependence plots, which help to interpret the tted models. In simulation studies we show that our gra- dient boosting procedure outperforms classical methods from quantile regression and extreme value theory, especially for high-dimensional predictor spaces and complex parameter response surfaces. An application to statistical post-processing of weather forecasts with precipitation data in the Netherlands is proposed.

This talk is an invited talk at EVA 2021. View the programme here.