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  • 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
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