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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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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 |
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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 |