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

Hung Tan Thai Nguyen, Harald Bernhard and Zhangsheng Lai

No a6zj4, Earth Arxiv from Center for Open Science

Abstract: 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.

Date: 2018-02-25
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Persistent link: https://EconPapers.repec.org/RePEc:osf:eartha:a6zj4

DOI: 10.31219/osf.io/a6zj4

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