A Review on Quantile Regression for Stochastic Computer Experiments
Machine Learning
2020-01-22 v4 Machine Learning
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.
Cite
@article{arxiv.1901.07874,
title = {A Review on Quantile Regression for Stochastic Computer Experiments},
author = {Léonard Torossian and Victor Picheny and Robert Faivre and Aurélien Garivier},
journal= {arXiv preprint arXiv:1901.07874},
year = {2020}
}