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

0548702 - ÚI 2022 CZ eng A - Abstrakt
Kalina, Jan - Vidnerová, Petra
Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election.
14th International Scientific Conference RELIK 2021. Book of Abstracts. Prague: Prague University of Economics and Business, 2021 - (Vrabcová, J.; Langhamrová, J.). s. 33-33
[RELIK 2021: Reproduction of Human Capital - mutual links and connections. 04.11.2021-05.11.2021, Praha]
Institucionální podpora: RVO:67985807
Klíčová slova: linear regression * quantile regression * robustness * outliers * elections results

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.
Trvalý link: http://hdl.handle.net/11104/0324755