Statistical learning for recommending (robust) nonlinear regression methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00520199" target="_blank" >RIV/67985556:_____/19:00520199 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/19:00511819
Result on the web
<a href="https://content.sciendo.com/view/journals/jamsi/15/2/article-p47.xml" target="_blank" >https://content.sciendo.com/view/journals/jamsi/15/2/article-p47.xml</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2478/jamsi-2019-0008" target="_blank" >10.2478/jamsi-2019-0008</a>
Alternative languages
Result language
angličtina
Original language name
Statistical learning for recommending (robust) nonlinear regression methods
Original language description
We are interested in comparing the performance of various nonlinear estimators of parameters of the standard nonlinear regression model. While the standard nonlinear least squares estimator is vulnerable to the presence of outlying measurements in the data, there exist several robust alternatives. However, it is not clear which estimator should be used for a given dataset and this question remains extremely difficult (or perhaps infeasible) to be answered theoretically. Metalearning represents a computationally intensive methodology for optimal selection of algorithms (or methods) and is used here to predict the most suitable nonlinear estimator for a particular dataset. The classification rule is learned over a training database of 24 publicly available datasets. The results of the primary learning give an interesting argument in favor of the nonlinear least weighted squares estimator, which turns out to be the most suitable one for the majority of datasets. The subsequent metalearning reveals that tests of normality and heteroscedasticity play a crucial role in finding the most suitable nonlinear estimator.n
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of applied mathematics, statistics and informatics
ISSN
1336-9180
e-ISSN
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Volume of the periodical
15
Issue of the periodical within the volume
2
Country of publishing house
SK - SLOVAKIA
Number of pages
13
Pages from-to
47-59
UT code for WoS article
000503976200004
EID of the result in the Scopus database
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