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

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  • Nguyen, Hung Tan Thai
  • Bernhard, Harald
  • Lai, Zhangsheng

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.

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  • Nguyen, Hung Tan Thai & Bernhard, Harald & Lai, Zhangsheng, 2018. "Bootstrapped high quantile estimation --- An experiment with scarce precipitation data," Earth Arxiv a6zj4, Center for Open Science.
    • Handle: RePEc:osf:eartha:a6zj4
    DOI: 10.31219/osf.io/a6zj4
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