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Original title:
Diagnostics for Robust Regression: Linear Versus Nonlinear Model
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /10./, Prague (CZ), 2016-12-14 / 2016-12-16
Year: 2016
Language: eng
Abstract: Robust statistical methods represent important tools for estimating parameters in linear as well as nonlinear econometric models. In contrary to the least squares, they do not suffer from vulnerability to the presence of outlying measurements in the data. Nevertheless, they need to be accompanied by diagnostic tools for verifying their assumptions. In this paper, we propose the asymptotic Goldfeld-Quandt test for the regression median. It allows to formulate a natural procedure for models with heteroscedastic disturbances, which is again based on the regression median. Further, we pay attention to nonlinear regression model. We focus on the nonlinear least weighted squares estimator, which is one of recently proposed robust estimators of parameters in a nonlinear regression. We study residuals of the estimator and use a numerical simulation to reveal that they can be severely heteroscedastic also for data generated from a model with homoscedastic disturbances. Thus, we give a warning that standard residuals of the robust nonlinear estimator may produce misleading results if used for the standard diagnostic tools
Keywords: diagnostic tools; nonlinear regression; outliers; residuals; robust estimation
Project no.: GA13-01930S (CEP), Neuron
Funding provider: GA ČR, Nadační fond na podporu vědy
Host item entry: The 10th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-10-0
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2016/article/3-Kalina-Jan-paper.pdf
Original record: http://hdl.handle.net/11104/0265798
Permalink: http://www.nusl.cz/ntk/nusl-261699
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /10./, Prague (CZ), 2016-12-14 / 2016-12-16
Year: 2016
Language: eng
Abstract: Robust statistical methods represent important tools for estimating parameters in linear as well as nonlinear econometric models. In contrary to the least squares, they do not suffer from vulnerability to the presence of outlying measurements in the data. Nevertheless, they need to be accompanied by diagnostic tools for verifying their assumptions. In this paper, we propose the asymptotic Goldfeld-Quandt test for the regression median. It allows to formulate a natural procedure for models with heteroscedastic disturbances, which is again based on the regression median. Further, we pay attention to nonlinear regression model. We focus on the nonlinear least weighted squares estimator, which is one of recently proposed robust estimators of parameters in a nonlinear regression. We study residuals of the estimator and use a numerical simulation to reveal that they can be severely heteroscedastic also for data generated from a model with homoscedastic disturbances. Thus, we give a warning that standard residuals of the robust nonlinear estimator may produce misleading results if used for the standard diagnostic tools
Keywords: diagnostic tools; nonlinear regression; outliers; residuals; robust estimation
Project no.: GA13-01930S (CEP), Neuron
Funding provider: GA ČR, Nadační fond na podporu vědy
Host item entry: The 10th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-10-0
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2016/article/3-Kalina-Jan-paper.pdf
Original record: http://hdl.handle.net/11104/0265798
Permalink: http://www.nusl.cz/ntk/nusl-261699
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2017-01-04, last modified 2023-12-06