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Estimation of a density using an improved surrogate model

Kohler, Michael ; Krzyzak, Adam (2021)
Estimation of a density using an improved surrogate model.
In: Electronic Journal of Statistics, 15 (1)
doi: 10.1214/20-EJS1774
Article, Bibliographie

Abstract

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.

Item Type: Article
Erschienen: 2021
Creators: Kohler, Michael ; Krzyzak, Adam
Type of entry: Bibliographie
Title: Estimation of a density using an improved surrogate model
Language: English
Date: 20 January 2021
Publisher: Institute of Mathematical Statistics
Journal or Publication Title: Electronic Journal of Statistics
Volume of the journal: 15
Issue Number: 1
DOI: 10.1214/20-EJS1774
URL / URN: https://projecteuclid.org/euclid.ejs/1611046878
Abstract:

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.

Divisions: 04 Department of Mathematics
04 Department of Mathematics > Stochastik
Date Deposited: 09 Feb 2021 09:17
Last Modified: 09 Feb 2021 09:17
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