METHODS FOR APPROXIMATING DISTRIBUTION OF UNKNOWN PARAMETER ESTIMATES WITH APPLICATION IN MATERIAL THERMOPHYSICS
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00351635" target="_blank" >RIV/68407700:21110/21:00351635 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1615/Int.J.UncertaintyQuantification.2021033482" target="_blank" >https://doi.org/10.1615/Int.J.UncertaintyQuantification.2021033482</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1615/Int.J.UncertaintyQuantification.2021033482" target="_blank" >10.1615/Int.J.UncertaintyQuantification.2021033482</a>
Alternative languages
Result language
angličtina
Original language name
METHODS FOR APPROXIMATING DISTRIBUTION OF UNKNOWN PARAMETER ESTIMATES WITH APPLICATION IN MATERIAL THERMOPHYSICS
Original language description
This paper discusses and compares three methods for approximating a joint probability distribution of least-squares estimates of parameters of interest in nonlinear regression. A joint distribution provides complete information about a random fluctuation of the estimates around their true values and can be used for computing arbitrary criterion values in order to assess accuracy of estimates in experimental design problems. Besides an approximate normal distribution and an approximate distribution obtained by numerical optimization of the utility function for the repeatedly simulated model, an approximate probability density derived by a differential geometry is recommended. To demonstrate the computational feasibility of the proposed methods, all three approaches are applied to several simplified versions of a numerical experiment to identify thermophysical parameters using a model with additional random parameters. The examples presented here illustrate how the suggested methods differ, including in terms of computational complexity.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal for Uncertainty Quantification
ISSN
2152-5080
e-ISSN
2152-5099
Volume of the periodical
11
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
31-47
UT code for WoS article
000729611800002
EID of the result in the Scopus database
2-s2.0-85120707360