Statistical learning for recommending (robust) nonlinear regression methods

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/67985807:_____/19:00511819

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical learning for recommending (robust) nonlinear regression methods

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Statistical learning for recommending (robust) nonlinear regression methods

Popis výsledku anglicky

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

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Journal of applied mathematics, statistics and informatics

ISSN

1336-9180

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

13

Strana od-do

47-59

Kód UT WoS článku

000503976200004

EID výsledku v databázi Scopus

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