Numerical Methods of Optimum Experimental Design Based on a Second-Order Approximation of Confidence Regions

Numerical Methods of Optimum Experimental Design Based on a Second-Order Approximation of Confidence Regions

A successful application of model-based simulation and optimization of dynamic processes requires an exact calibration of the underlying mathematical models. Here, a fundamental task is the estimation of unknown and nature given model coefficients by means of real observations. After an appropriat...

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Bibliographic Details
Main Author: Nattermann, Max
Contributors: Kostina, Ekaterina (Prof. Dr.) (Thesis advisor)
Format: Doctoral Thesis
Language:English
Published: Philipps-Universität Marburg 2014
Subjects:
Sensitivity Analysis
Robuste Versuchsplanung
DoE
Konfidenzgebiete
Sensitivitätsanalyse
Parameter Estimation
Parameterschätzung
Mathematik
Robustification
Confidence Regions
Optimum Experimental Design
Optimale Versuchsplanung
Mathematics
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Call Number: urn:nbn:de:hebis:04-z2015-00540
Publication Date: 2015-02-23
Date of Acceptance: 2014-10-27
Downloads: 75 (2024), 116 (2023), 122 (2022), 262 (2021), 195 (2020), 133 (2019), 108 (2018)
License: https://rightsstatements.org/vocab/InC-NC/1.0/
Access URL: https://archiv.ub.uni-marburg.de/diss/z2015/0054
https://doi.org/10.17192/z2015.0054

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