Robust Box-Cox transformations based on minimum residual autocorrelation

Response transformations are a popular approach to adapt data to a linear regression model. The regression coefficients, as well as the parameter defining the transformation, are often estimated by maximum likelihood assuming homoscedastic normal errors. Unfortunately, consistency to the true parame...

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Detalles Bibliográficos
Autores principales: Marazzi, A., Yohai, V.J.
Formato: JOUR
Materias:
Box-Cox transformation
Heteroscedasticity
Robust estimation
Computational methods
Error analysis
Mathematical models
Optimization
Regression analysis
Robustness (control systems)
Mathematical transformations
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_01679473_v50_n10_p2752_Marazzi
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_01679473_v50_n10_p2752_Marazzi
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spelling todo:paper_01679473_v50_n10_p2752_Marazzi2023-10-03T15:05:35Z Robust Box-Cox transformations based on minimum residual autocorrelation Marazzi, A. Yohai, V.J. Box-Cox transformation Heteroscedasticity Robust estimation Computational methods Error analysis Mathematical models Optimization Regression analysis Robustness (control systems) Box-Cox transformation Heteroscedasticity Robust estimation Mathematical transformations Response transformations are a popular approach to adapt data to a linear regression model. The regression coefficients, as well as the parameter defining the transformation, are often estimated by maximum likelihood assuming homoscedastic normal errors. Unfortunately, consistency to the true parameters holds only if the assumptions of normality and homoscedasticity are satisfied. In addition, these estimates are nonrobust in the presence of outliers. New estimates are proposed, which are robust and consistent even if the assumptions of normality and homoscedasticity do not hold. These estimates are based on the minimization of a robust measure of residual autocorrelation. © 2005 Elsevier B.V. All rights reserved. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01679473_v50_n10_p2752_Marazzi
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Box-Cox transformation
Heteroscedasticity
Robust estimation
Computational methods
Error analysis
Mathematical models
Optimization
Regression analysis
Robustness (control systems)
Box-Cox transformation
Heteroscedasticity
Robust estimation
Mathematical transformations
spellingShingle Box-Cox transformation
Heteroscedasticity
Robust estimation
Computational methods
Error analysis
Mathematical models
Optimization
Regression analysis
Robustness (control systems)
Box-Cox transformation
Heteroscedasticity
Robust estimation
Mathematical transformations
Marazzi, A.
Yohai, V.J.
Robust Box-Cox transformations based on minimum residual autocorrelation
topic_facet Box-Cox transformation
Heteroscedasticity
Robust estimation
Computational methods
Error analysis
Mathematical models
Optimization
Regression analysis
Robustness (control systems)
Box-Cox transformation
Heteroscedasticity
Robust estimation
Mathematical transformations
description Response transformations are a popular approach to adapt data to a linear regression model. The regression coefficients, as well as the parameter defining the transformation, are often estimated by maximum likelihood assuming homoscedastic normal errors. Unfortunately, consistency to the true parameters holds only if the assumptions of normality and homoscedasticity are satisfied. In addition, these estimates are nonrobust in the presence of outliers. New estimates are proposed, which are robust and consistent even if the assumptions of normality and homoscedasticity do not hold. These estimates are based on the minimization of a robust measure of residual autocorrelation. © 2005 Elsevier B.V. All rights reserved.
format JOUR
author Marazzi, A.
Yohai, V.J.
author_facet Marazzi, A.
Yohai, V.J.
author_sort Marazzi, A.
title Robust Box-Cox transformations based on minimum residual autocorrelation
title_short Robust Box-Cox transformations based on minimum residual autocorrelation
title_full Robust Box-Cox transformations based on minimum residual autocorrelation
title_fullStr Robust Box-Cox transformations based on minimum residual autocorrelation
title_full_unstemmed Robust Box-Cox transformations based on minimum residual autocorrelation
title_sort robust box-cox transformations based on minimum residual autocorrelation
url http://hdl.handle.net/20.500.12110/paper_01679473_v50_n10_p2752_Marazzi
work_keys_str_mv AT marazzia robustboxcoxtransformationsbasedonminimumresidualautocorrelation
AT yohaivj robustboxcoxtransformationsbasedonminimumresidualautocorrelation
_version_ 1782029121797226496

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