OEFPIL: New Method and Software Tool for Fitting Nonlinear Functions to Correlated Data With Errors in Variables
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
OEFPIL: New Method and Software Tool for Fitting Nonlinear Functions to Correlated Data With Errors in Variables
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 14th International Conference on Measurement, MEASUREMENT 2023
ISBN
9798350312188
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
126-129
Publisher name
IEEE
Place of publication
New Jersey
Event location
Smolenice Castle
Event date
May 29, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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