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Estimation of a density using an improved surrogate model

Kohler, Michael and Krzyzak, Adam (2021):
Estimation of a density using an improved surrogate model.
In: Electronic Journal of Statistics, 15 (1), pp. 650-690. Institute of Mathematical Statistics, ISSN 1935-7524,
DOI: 10.1214/20-EJS1774,
[Article]

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 and Krzyzak, Adam
Title: Estimation of a density using an improved surrogate model
Language: English
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.

Journal or Publication Title: Electronic Journal of Statistics
Journal volume: 15
Number: 1
Publisher: Institute of Mathematical Statistics
Divisions: 04 Department of Mathematics
04 Department of Mathematics > Stochastik
Date Deposited: 09 Feb 2021 09:17
DOI: 10.1214/20-EJS1774
Official URL: https://projecteuclid.org/euclid.ejs/1611046878
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