# A Review on Quantile Regression for Stochastic Computer Experiments

## TL;DR

This paper empirically compares six quantile regression strategies for stochastic computer experiments, providing insights and guidelines for selecting the most suitable method based on problem characteristics.

## Contribution

It introduces a comprehensive empirical evaluation of diverse quantile regression metamodels, offering practical guidelines for method selection in stochastic experiments.

## Key findings

- Different metamodels perform variably depending on data characteristics.
- Guidelines help choose the best quantile regression method for specific problem settings.
- The study highlights the strengths and limitations of each approach.

## 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.