Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00555814" target="_blank" >RIV/67985556:_____/21:00555814 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/21:00553132

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election

Popis výsledku v původním jazyce

Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.

Název v anglickém jazyce

Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election

Popis výsledku anglicky

Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

RELIK 2021. Conference Proceedings

ISBN

978-80-245-2429-0

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

332-341

Název nakladatele

Prague University of Economics and Business

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

4. 11. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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