Title: A Review on Quantile Regression for Stochastic Computer Experiments,
Abstract: We report on an empirical study of the main strategies for conditional
quantile estimation 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
quantile order and the value of the probability density function in the
neighborhood of the quantile. The metamodels studied reveal good contrasts in
our set of 480 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.