Cargando…

Robust Z-Estimators for Semiparametric Moment Condition Models

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that...

Descripción completa

Detalles Bibliográficos
Autor principal: Toma, Aida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377762/
https://www.ncbi.nlm.nih.gov/pubmed/37509960
http://dx.doi.org/10.3390/e25071013
_version_ 1785079597563379712
author Toma, Aida
author_facet Toma, Aida
author_sort Toma, Aida
collection PubMed
description In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.
format Online
Article
Text
id pubmed-10377762
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-103777622023-07-29 Robust Z-Estimators for Semiparametric Moment Condition Models Toma, Aida Entropy (Basel) Article In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated. MDPI 2023-06-30 /pmc/articles/PMC10377762/ /pubmed/37509960 http://dx.doi.org/10.3390/e25071013 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Toma, Aida
Robust Z-Estimators for Semiparametric Moment Condition Models
title Robust Z-Estimators for Semiparametric Moment Condition Models
title_full Robust Z-Estimators for Semiparametric Moment Condition Models
title_fullStr Robust Z-Estimators for Semiparametric Moment Condition Models
title_full_unstemmed Robust Z-Estimators for Semiparametric Moment Condition Models
title_short Robust Z-Estimators for Semiparametric Moment Condition Models
title_sort robust z-estimators for semiparametric moment condition models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377762/
https://www.ncbi.nlm.nih.gov/pubmed/37509960
http://dx.doi.org/10.3390/e25071013
work_keys_str_mv AT tomaaida robustzestimatorsforsemiparametricmomentconditionmodels