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A Review on Quantile Regression for Stochastic Computer Experiments

Year: 2019

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1901.07874

Authors: Torossian, Léonard, Picheny, Victor, Faivre, Robert, Garivier, Aurélien

Language: English

Topic: Optimal Experimental Design Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4288624812

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