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Original title:
A Bootstrap Comparison of Robust Regression Estimators
Authors: Kalina, Jan ; Janáček, Patrik
Document type: Papers
Conference/Event: MME 2022: International Conference on Mathematical Methods in Economics /40./, Jihlava (CZ), 20220907
Year: 2022
Language: eng
Abstract: 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.
Keywords: bootstrap hypothesis testing; linear regression; nonparametric bootstrap; robust estimation
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: Mathematical Methods in Economics 2022: Proceedings, ISBN 978-80-88064-62-6
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://mme2022.vspj.cz/download/proceedings-4.pdf
Original record: https://hdl.handle.net/11104/0336179
Permalink: http://www.nusl.cz/ntk/nusl-511783
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan ; Janáček, Patrik
Document type: Papers
Conference/Event: MME 2022: International Conference on Mathematical Methods in Economics /40./, Jihlava (CZ), 20220907
Year: 2022
Language: eng
Abstract: 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.
Keywords: bootstrap hypothesis testing; linear regression; nonparametric bootstrap; robust estimation
Project no.: GA21-05325S (CEP)
Funding provider: GA ČR
Host item entry: Mathematical Methods in Economics 2022: Proceedings, ISBN 978-80-88064-62-6
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://mme2022.vspj.cz/download/proceedings-4.pdf
Original record: https://hdl.handle.net/11104/0336179
Permalink: http://www.nusl.cz/ntk/nusl-511783
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2022-11-27, last modified 2024-04-15