METHODS FOR APPROXIMATING DISTRIBUTION OF UNKNOWN PARAMETER ESTIMATES WITH APPLICATION IN MATERIAL THERMOPHYSICS

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

METHODS FOR APPROXIMATING DISTRIBUTION OF UNKNOWN PARAMETER ESTIMATES WITH APPLICATION IN MATERIAL THERMOPHYSICS

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

METHODS FOR APPROXIMATING DISTRIBUTION OF UNKNOWN PARAMETER ESTIMATES WITH APPLICATION IN MATERIAL THERMOPHYSICS

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal for Uncertainty Quantification

ISSN

2152-5080

e-ISSN

2152-5099

Svazek periodika

11

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

31-47

Kód UT WoS článku

000729611800002

EID výsledku v databázi Scopus

2-s2.0-85120707360