OEFPIL: New Method and Software Tool for Fitting Nonlinear Functions to Correlated Data With Errors in Variables

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00141688" target="_blank" >RIV/00216224:14310/23:00141688 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.23919/MEASUREMENT59122.2023.10164444" target="_blank" >https://doi.org/10.23919/MEASUREMENT59122.2023.10164444</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164444" target="_blank" >10.23919/MEASUREMENT59122.2023.10164444</a>

Jazyk výsledku

angličtina

Název v původním jazyce

OEFPIL: New Method and Software Tool for Fitting Nonlinear Functions to Correlated Data With Errors in Variables

Popis výsledku v původním jazyce

We present a new method, called OEFPIL, as well as its software implementation for nonlinear function fitting to data with errors in variables where correlation, both within variables and among variables, might be present. In principle, OEFPIL can be employed for fitting both explicit and implicit functions of any number of variables. Importantly, apart from the parameter estimates, OEFPIL also yields their covariance matrix, required for further analyses. Multiple comparisons with existing methods on various types of problems, some of which are presented in this paper, have shown excellent agreement between OEFPIL and other methods.

Název v anglickém jazyce

OEFPIL: New Method and Software Tool for Fitting Nonlinear Functions to Correlated Data With Errors in Variables

Popis výsledku anglicky

We present a new method, called OEFPIL, as well as its software implementation for nonlinear function fitting to data with errors in variables where correlation, both within variables and among variables, might be present. In principle, OEFPIL can be employed for fitting both explicit and implicit functions of any number of variables. Importantly, apart from the parameter estimates, OEFPIL also yields their covariance matrix, required for further analyses. Multiple comparisons with existing methods on various types of problems, some of which are presented in this paper, have shown excellent agreement between OEFPIL and other methods.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 14th International Conference on Measurement, MEASUREMENT 2023

ISBN

9798350312188

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

126-129

Název nakladatele

IEEE

Místo vydání

New Jersey

Místo konání akce

Smolenice Castle

Datum konání akce

29. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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