Robust inference for nonlinear regression models

A family of weighted estimators of the regression parameter under a nonlinear model is introduced. The proposed weighted estimators are computed through a four-step MM-procedure, and the given approach allows for possible missing responses. Under mild conditions, the proposed estimators turn to be c...

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Detalles Bibliográficos
Autores principales: Bianco, A.M., Spano, P.M.
Formato: INPR
Materias:
Missing at random
MM-procedure
Nonlinear regression
Robust estimation
Robust hypothesis testing
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_11330686_v_n_p1_Bianco
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_11330686_v_n_p1_Bianco
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spelling todo:paper_11330686_v_n_p1_Bianco2023-10-03T16:07:57Z Robust inference for nonlinear regression models Bianco, A.M. Spano, P.M. Missing at random MM-procedure Nonlinear regression Robust estimation Robust hypothesis testing A family of weighted estimators of the regression parameter under a nonlinear model is introduced. The proposed weighted estimators are computed through a four-step MM-procedure, and the given approach allows for possible missing responses. Under mild conditions, the proposed estimators turn to be consistent and asymptotically normal. A robust Wald-type test statistic based on this family of estimators is also provided, and its asymptotic distribution is derived under the null and contiguous hypotheses. The local robustness of the proposed procedures is studied via the influence function analysis, and the finite sample behaviour of the estimators and tests is investigated through a Monte Carlo study in different contaminated scenarios. An application to an environmental data set illustrates the procedure. © 2017 Sociedad de Estadística e Investigación Operativa INPR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_11330686_v_n_p1_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 Missing at random
MM-procedure
Nonlinear regression
Robust estimation
Robust hypothesis testing
spellingShingle Missing at random
MM-procedure
Nonlinear regression
Robust estimation
Robust hypothesis testing
Bianco, A.M.
Spano, P.M.
Robust inference for nonlinear regression models
topic_facet Missing at random
MM-procedure
Nonlinear regression
Robust estimation
Robust hypothesis testing
description A family of weighted estimators of the regression parameter under a nonlinear model is introduced. The proposed weighted estimators are computed through a four-step MM-procedure, and the given approach allows for possible missing responses. Under mild conditions, the proposed estimators turn to be consistent and asymptotically normal. A robust Wald-type test statistic based on this family of estimators is also provided, and its asymptotic distribution is derived under the null and contiguous hypotheses. The local robustness of the proposed procedures is studied via the influence function analysis, and the finite sample behaviour of the estimators and tests is investigated through a Monte Carlo study in different contaminated scenarios. An application to an environmental data set illustrates the procedure. © 2017 Sociedad de Estadística e Investigación Operativa
format INPR
author Bianco, A.M.
Spano, P.M.
author_facet Bianco, A.M.
Spano, P.M.
author_sort Bianco, A.M.
title Robust inference for nonlinear regression models
title_short Robust inference for nonlinear regression models
title_full Robust inference for nonlinear regression models
title_fullStr Robust inference for nonlinear regression models
title_full_unstemmed Robust inference for nonlinear regression models
title_sort robust inference for nonlinear regression models
url http://hdl.handle.net/20.500.12110/paper_11330686_v_n_p1_Bianco
work_keys_str_mv AT biancoam robustinferencefornonlinearregressionmodels
AT spanopm robustinferencefornonlinearregressionmodels
_version_ 1807314611054051328

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