Applications of Iterated Linearization for non-linear errors-in-variable regression to metrological data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00177016%3A_____%2F25%3AN0000116" target="_blank" >RIV/00177016:_____/25:N0000116 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/25:00144262

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2665917424007050" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2665917424007050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measen.2024.101729" target="_blank" >10.1016/j.measen.2024.101729</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Applications of Iterated Linearization for non-linear errors-in-variable regression to metrological data

Popis výsledku v původním jazyce

Correct data processing and uncertainty assessment is crucial for metrology. One of the most common methods used is function fitting using non-linear least squares. This numerical method has been implemented in probably all data processing software and is quick and easy to use. Unfortunately, it has its limitations – notably it works only for very simple models of the uncertainties present in the system. Uncertainties in the dependent variable cannot be taken into account, and neither do correlations. Errors-in-variables models minimize a generalized distance of the points from the fitted function. The metric used to compute the distance is given by the inverse of the covariance matrix. Thus, the estimates of the uncertainties entering the computation may affect the resulting estimates of the fitted parameters. In this contribution we illustrate the use of an iterative EIV algorithm on an example from nanoindentation, especially the sensitivity of the results to input data including uncertainties.

Název v anglickém jazyce

Applications of Iterated Linearization for non-linear errors-in-variable regression to metrological data

Popis výsledku anglicky

Correct data processing and uncertainty assessment is crucial for metrology. One of the most common methods used is function fitting using non-linear least squares. This numerical method has been implemented in probably all data processing software and is quick and easy to use. Unfortunately, it has its limitations – notably it works only for very simple models of the uncertainties present in the system. Uncertainties in the dependent variable cannot be taken into account, and neither do correlations. Errors-in-variables models minimize a generalized distance of the points from the fitted function. The metric used to compute the distance is given by the inverse of the covariance matrix. Thus, the estimates of the uncertainties entering the computation may affect the resulting estimates of the fitted parameters. In this contribution we illustrate the use of an iterative EIV algorithm on an example from nanoindentation, especially the sensitivity of the results to input data including uncertainties.

Druh

D - Stať ve sborníku

CEP obor

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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

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the XXIV IMEKO World Congress

ISBN

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ISSN

2665-9174

e-ISSN

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Počet stran výsledku

5

Strana od-do

—

Název nakladatele

—

Místo vydání

—

Místo konání akce

Hamburg

Datum konání akce

26. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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