Imperial College London
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Professor Axel Gandy

Faculty of Natural Sciences, Department of Mathematics

Head of Department of Mathematics & Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8518a.gandy Website

 
 
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Location

 

644Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gandy:2022:10.1007/s10182-021-00421-9,
author = {Gandy, A and Jana, K and Veraart, A},
doi = {10.1007/s10182-021-00421-9},
journal = {AStA Advances in Statistical Analysis},
pages = {527--544},
title = {Scoring predictions at extreme quantiles},
url = {http://dx.doi.org/10.1007/s10182-021-00421-9},
volume = {106},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Prediction of quantiles at extreme tails is of interest in numerousapplications. Extreme value modelling provides various competing predictorsfor this point prediction problem. A common method of assessment of a setof competing predictors is to evaluate their predictive performance in a givensituation. However, due to the extreme nature of this inference problem, it canbe possible that the predicted quantiles are not seen in the historical records,particularly when the sample size is small. This situation poses a problem tothe validation of the prediction with its realisation. In this article, we proposetwo non-parametric scoring approaches to assess extreme quantile predictionmechanisms. The proposed assessment methods are based on predicting a sequence of equally extreme quantiles on different parts of the data. We thenuse the quantile scoring function to evaluate the competing predictors. Theperformance of the scoring methods is compared with the conventional scoring method and the superiority of the former methods are demonstrated in asimulation study. The methods are then applied to reanalyse cyber Netflowdata from Los Alamos National Laboratory and daily precipitation data at astation in California available from Global Historical Climatology Network.
AU  - Gandy,A
AU  - Jana,K
AU  - Veraart,A
DO  - 10.1007/s10182-021-00421-9
EP  - 544
PY  - 2022///
SN  - 0002-6018
SP  - 527
TI  - Scoring predictions at extreme quantiles
T2  - AStA Advances in Statistical Analysis
UR  - http://dx.doi.org/10.1007/s10182-021-00421-9
UR  - https://link.springer.com/article/10.1007/s10182-021-00421-9
UR  - http://hdl.handle.net/10044/1/92555
VL  - 106
ER  -
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