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Original title:
Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Authors: Kalina, Jan ; Vidnerová, Petra
Document type: Papers
Conference/Event: RELIK 2021: Reproduction of Human Capital - mutual links and connections, Praha (CZ), 20211104
Year: 2021
Language: eng
Abstract: 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.
Keywords: elections results; linear regression; outliers; quantile regression; robustness
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: RELIK 2021. Conference Proceedings, ISBN 978-80-245-2429-0
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://relik.vse.cz/2021/download/pdf/381-Vidnerova-Petra-paper.pdf
Original record: http://hdl.handle.net/11104/0328139
Permalink: http://www.nusl.cz/ntk/nusl-508469
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan ; Vidnerová, Petra
Document type: Papers
Conference/Event: RELIK 2021: Reproduction of Human Capital - mutual links and connections, Praha (CZ), 20211104
Year: 2021
Language: eng
Abstract: 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.
Keywords: elections results; linear regression; outliers; quantile regression; robustness
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: RELIK 2021. Conference Proceedings, ISBN 978-80-245-2429-0
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://relik.vse.cz/2021/download/pdf/381-Vidnerova-Petra-paper.pdf
Original record: http://hdl.handle.net/11104/0328139
Permalink: http://www.nusl.cz/ntk/nusl-508469
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2022-09-28, last modified 2023-12-06