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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Xehetasun bibliografikoak
Egile nagusia: Nattermann, Max
Beste egile batzuk: Kostina, Ekaterina (Prof. Dr.) (Tesi aholkularia)
Formatua: Dissertation
Hizkuntza:ingelesa
Argitaratua: Philipps-Universität Marburg 2014
Gaiak:
Sensitivity Analysis
Robuste Versuchsplanung
DoE
Konfidenzgebiete
Sensitivitätsanalyse
Parameter Estimation
Parameterschätzung
Mathematik
Robustification
Confidence Regions
Optimum Experimental Design
Optimale Versuchsplanung
Mathematics
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Aleari buruzko argibideak
Sailkapena: urn:nbn:de:hebis:04-z2015-00540
Argitaratze data: 2015-02-23
Datum der Annahme: 2014-10-27
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Lizenz: https://rightsstatements.org/vocab/InC-NC/1.0/
URL sarbidea: https://archiv.ub.uni-marburg.de/diss/z2015/0054
https://doi.org/10.17192/z2015.0054

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