文章基本信息

标题：Estimation of a density using an improved surrogate model
本地全文：下载
作者：Michael Kohler ; Adam Krzyżak
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2021
卷号：15
期号：1
页码：650-690
DOI：10.1214/20-EJS1774
语种：English
出版社：Institute of Mathematical Statistics
摘要：Quantification of uncertainty of a technical system is often based on a surrogate model of a corresponding simulation model. In any application the simulation model will not describe the reality perfectly, and consequently also the surrogate model will be imperfect. In this article we show how observed data of the real technical system can be used to improve such a surrogate model, and we analyze the rate of convergence of density estimates based on the improved surrogate model. The results are illustrated by applying the estimates to simulated and real data.
关键词：Density estimation;imperfect models;$L_{1}$ error;surrogate models;uncertainty quantification