A Bootstrap Comparison of Robust Regression Estimators

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00564518" target="_blank" >RIV/67985556:_____/22:00564518 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/22:00564518 RIV/67985556:_____/22:00583572

Výsledek na webu

<a href="https://mme2022.vspj.cz/download/proceedings-4.pdf" target="_blank" >https://mme2022.vspj.cz/download/proceedings-4.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Bootstrap Comparison of Robust Regression Estimators

Popis výsledku v původním jazyce

The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more preferable alternatives. It has been repeatedly recommended to use the least squares together with a robust estimator, where the latter is understood as a diagnostic tool for the former. In other words, only if the robust estimator yields a very different result, the user should investigate the dataset closer and search for explanations. For this purpose, a hypothesis test of equality of the means of two alternative linear regression estimators is proposed here based on nonparametric bootstrap. The performance of the test is presented on three real economic datasets with small samples. Robust estimates turn out not to be significantly different from non-robust estimates in the selected datasets. Still, robust estimation is beneficial in these datasets and the experiments illustrate one of possible ways of exploiting the bootstrap methodology in regression modeling. The bootstrap test could be easily extended to nonlinear regression models.

Název v anglickém jazyce

A Bootstrap Comparison of Robust Regression Estimators

Popis výsledku anglicky

The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more preferable alternatives. It has been repeatedly recommended to use the least squares together with a robust estimator, where the latter is understood as a diagnostic tool for the former. In other words, only if the robust estimator yields a very different result, the user should investigate the dataset closer and search for explanations. For this purpose, a hypothesis test of equality of the means of two alternative linear regression estimators is proposed here based on nonparametric bootstrap. The performance of the test is presented on three real economic datasets with small samples. Robust estimates turn out not to be significantly different from non-robust estimates in the selected datasets. Still, robust estimation is beneficial in these datasets and the experiments illustrate one of possible ways of exploiting the bootstrap methodology in regression modeling. The bootstrap test could be easily extended to nonlinear regression models.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA21-05325S" target="_blank" >GA21-05325S: Moderní neparametrické metody v ekonometrii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Mathematical Methods in Economics 2022: Proceedings

ISBN

978-80-88064-62-6

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

161-167

Název nakladatele

College of Polytechnics Jihlava

Místo vydání

Jihlava

Místo konání akce

Jihlava

Datum konání akce

7. 9. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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