Robust estimation for linear regression with asymmetric errors

The authors propose a new class of robust estimators for the parameters of a regression model in which the distribution of the error terms belongs to a class of exponential families including the log-gamma distribution. These estimates, which are a natural extension of the MM-estimates for ordinary...

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Detalles Bibliográficos
Autores principales: Bianco, A.M., Garcia Ben, M., Yohai, V.J.
Formato: JOUR
Materias:
Log-gamma regression
M-estimates
Robust estimates
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03195724_v33_n4_p511_Bianco
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_03195724_v33_n4_p511_Bianco
record_format dspace
spelling todo:paper_03195724_v33_n4_p511_Bianco2023-10-03T15:23:14Z Robust estimation for linear regression with asymmetric errors Bianco, A.M. Garcia Ben, M. Yohai, V.J. Log-gamma regression M-estimates Robust estimates The authors propose a new class of robust estimators for the parameters of a regression model in which the distribution of the error terms belongs to a class of exponential families including the log-gamma distribution. These estimates, which are a natural extension of the MM-estimates for ordinary regression, may combine simultaneously high asymptotic efficiency and a high breakdown point. The authors prove the consistency and derive the asymptotic normal distribution of these estimates. A Monte Carlo study allows them to assess the efficiency and robustness of these estimates for finite samples. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03195724_v33_n4_p511_Bianco
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Log-gamma regression
M-estimates
Robust estimates
spellingShingle Log-gamma regression
M-estimates
Robust estimates
Bianco, A.M.
Garcia Ben, M.
Yohai, V.J.
Robust estimation for linear regression with asymmetric errors
topic_facet Log-gamma regression
M-estimates
Robust estimates
description The authors propose a new class of robust estimators for the parameters of a regression model in which the distribution of the error terms belongs to a class of exponential families including the log-gamma distribution. These estimates, which are a natural extension of the MM-estimates for ordinary regression, may combine simultaneously high asymptotic efficiency and a high breakdown point. The authors prove the consistency and derive the asymptotic normal distribution of these estimates. A Monte Carlo study allows them to assess the efficiency and robustness of these estimates for finite samples.
format JOUR
author Bianco, A.M.
Garcia Ben, M.
Yohai, V.J.
author_facet Bianco, A.M.
Garcia Ben, M.
Yohai, V.J.
author_sort Bianco, A.M.
title Robust estimation for linear regression with asymmetric errors
title_short Robust estimation for linear regression with asymmetric errors
title_full Robust estimation for linear regression with asymmetric errors
title_fullStr Robust estimation for linear regression with asymmetric errors
title_full_unstemmed Robust estimation for linear regression with asymmetric errors
title_sort robust estimation for linear regression with asymmetric errors
url http://hdl.handle.net/20.500.12110/paper_03195724_v33_n4_p511_Bianco
work_keys_str_mv AT biancoam robustestimationforlinearregressionwithasymmetricerrors
AT garciabenm robustestimationforlinearregressionwithasymmetricerrors
AT yohaivj robustestimationforlinearregressionwithasymmetricerrors
_version_ 1807318051388915712

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