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A review on quantile regression for stochastic computer experiments

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  • Torossian, Léonard
  • Picheny, Victor
  • Faivre, Robert
  • Garivier, Aurélien

Abstract

We report on an empirical study of the main strategies for quantile regression in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are presented, divided into three categories based on order statistics, functional approaches, and those of Bayesian inspiration. The metamodels are tested on several problems characterized by the size of the training set, the input dimension, the signal-to-noise ratio and the value of the probability density function at the targeted quantile. The metamodels studied reveal good contrasts in our set of experiments, enabling several patterns to be extracted. Based on our results, guidelines are proposed to allow users to select the best method for a given problem.

Suggested Citation

  • Torossian, Léonard & Picheny, Victor & Faivre, Robert & Garivier, Aurélien, 2020. "A review on quantile regression for stochastic computer experiments," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:reensy:v:201:y:2020:i:c:s0951832019300638
    DOI: 10.1016/j.ress.2020.106858
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    References listed on IDEAS

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    1. Song, Bing & Zhang, Zhipeng & Qin, Yong & Liu, Xiang & Hu, Hao, 2022. "Quantitative analysis of freight train derailment severity with structured and unstructured data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    2. Manon Costa & Sébastien Gadat, 2021. "Non-asymptotic study of a recursive superquantile estimation algorithm," Post-Print hal-03610477, HAL.
    3. Gadat, Sébastien & Costa, Manon, 2020. "Non asymptotic controls on a stochastic algorithm for superquantile approximation," TSE Working Papers 20-1149, Toulouse School of Economics (TSE).

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