On estimation of surrogate models for multivariate computer experiments

DOI10.1007/s10463-017-0627-8zbMath1415.62017OpenAlexW2766868446MaRDI QIDQ1733118

Michael Kohler, Felix Heimrich, Adam Krzyżak, Benedikt Bauer

Publication date: 21 March 2019

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-017-0627-8

zbMATH Keywords

rate of convergenceneural networkscurse of dimensionalitycomputer experimentssurrogate modelsquantile estimatesnonparametric regression without noise in the dependent variable

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Applications of statistics in engineering and industry; control charts (62P30)

Related Items

Risk bounds when learning infinitely many response functions by ordinary linear regression, Analysis of the rate of convergence of two regression estimates defined by neural features which are easy to implement

Uses Software

• ElemStatLearn

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