Estimators Based on Ranks for Arma Models

In this paper we introduce a new family of robust estimators for ARMA models. These estimators are defined by replacing the residual sample autocovariances in the least squares equations by autocovariances based on ranks. The asymptotic normality of the proposed estimators is provided. The efficienc...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Ferretti, N.E., Yohai, V.J.
Formato: JOUR
Materias:
ARMA models
asymptotic relative efficiency
estimation based on ranks
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03610926_v20_n12_p3879_Ferretti
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_03610926_v20_n12_p3879_Ferretti
record_format dspace
spelling todo:paper_03610926_v20_n12_p3879_Ferretti2023-10-03T15:26:41Z Estimators Based on Ranks for Arma Models Ferretti, N.E. Yohai, V.J. ARMA models asymptotic relative efficiency estimation based on ranks In this paper we introduce a new family of robust estimators for ARMA models. These estimators are defined by replacing the residual sample autocovariances in the least squares equations by autocovariances based on ranks. The asymptotic normality of the proposed estimators is provided. The efficiency and robustness properties of these estimators are studied. An adequate choice of the score functions gives estimators which have high efficiency under normality and robustness in the presence of outliers. The score functions can also be chosen so that the resulting estimators are asymptotically as efficient as the maximum likelihood estimators for a given distribution. © 1991, Taylor & Francis Group, LLC. 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_03610926_v20_n12_p3879_Ferretti
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic ARMA models
asymptotic relative efficiency
estimation based on ranks
spellingShingle ARMA models
asymptotic relative efficiency
estimation based on ranks
Ferretti, N.E.
Yohai, V.J.
Estimators Based on Ranks for Arma Models
topic_facet ARMA models
asymptotic relative efficiency
estimation based on ranks
description In this paper we introduce a new family of robust estimators for ARMA models. These estimators are defined by replacing the residual sample autocovariances in the least squares equations by autocovariances based on ranks. The asymptotic normality of the proposed estimators is provided. The efficiency and robustness properties of these estimators are studied. An adequate choice of the score functions gives estimators which have high efficiency under normality and robustness in the presence of outliers. The score functions can also be chosen so that the resulting estimators are asymptotically as efficient as the maximum likelihood estimators for a given distribution. © 1991, Taylor & Francis Group, LLC. All rights reserved.
format JOUR
author Ferretti, N.E.
Yohai, V.J.
author_facet Ferretti, N.E.
Yohai, V.J.
author_sort Ferretti, N.E.
title Estimators Based on Ranks for Arma Models
title_short Estimators Based on Ranks for Arma Models
title_full Estimators Based on Ranks for Arma Models
title_fullStr Estimators Based on Ranks for Arma Models
title_full_unstemmed Estimators Based on Ranks for Arma Models
title_sort estimators based on ranks for arma models
url http://hdl.handle.net/20.500.12110/paper_03610926_v20_n12_p3879_Ferretti
work_keys_str_mv AT ferrettine estimatorsbasedonranksforarmamodels
AT yohaivj estimatorsbasedonranksforarmamodels
_version_ 1782030935134306304

Ejemplares similares