Bootstrapped high quantile estimation --- An experiment with scarce precipitation data

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint

Supplementary Files

Hung Tan Thai Nguyen, Harald Bernhard, Zhangsheng Lai


This paper details team SUTD’s effort when participating in the “Prediction of extremal precipitation” challenge. We propose a framework that combines the generalized Pareto distribution, a bootstrap resampling scheme and inverse distance weights to capture spatial dependence. Our method reduces the quantile loss functions by 55.1% as compared to a naive benchmark, and shows improvement across all months and all stations. The method works well even for stations without training data. Despite being simple, our method ranked fifth in the competition and our scores were very close to those of the winning teams. The framework is scalable and can be implemented easily by practising engineers.



Applied Statistics, Physical Sciences and Mathematics, Probability, Statistics and Probability


generalized Pareto distribution, Ungauged sites, extreme precipitations, inverse distance weights


Published: 2018-02-25 16:39


Academic Free License (AFL) 3.0