Robust location estimators in regression models with covariates and responses missing at random-Reference-Cited by-同舟云学术

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Robust location estimators in regression models with covariates and responses missing at random

Published:2020-10-01 Issue:4 Volume:32 Page:915-939
ISSN:1048-5252
Container-title:Journal of Nonparametric Statistics
language:en
Short-container-title:Journal of Nonparametric Statistics

Author:

Bianco Ana M.¹,Boente Graciela²,González-Manteiga Wenceslao³^ORCID,Pérez-González Ana⁴

Affiliation:

1. Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Buenos Aires, Argentina

2. Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and Instituto de Cálculo, CONICET, Buenos Aires, Argentina

3. Departamento de Estatística, Análise Matemática e Optimización, Facultad de Matemáticas, Universidad de Santiago de Compostela, Santiago de Compostela, Spain

4. Departamento de Estadística e Investigación Operativa, Universidad de Vigo, Ourense, Spain

Funder

ANPCyT

Universidad de Buenos Aires

Ministry of Economy and Competitiveness

Secretaría de Estado de Investigación, Desarrollo e Innovación

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10485252.2020.1834108

Reference28 articles.

1. Robust estimators in semiparametric partly linear regression models

2. Robust inference for nonlinear regression models

3. Robust estimation for linear regression with asymmetric errors

4. Estimation of the marginal location under a partially linear model with missing responses

5. Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Testing for linearity in scalar-on-function regression with responses missing at random;Computational Statistics;2024-01-03

2. A framework of nonparametric regression to predict natural gas demand;AIP Conference Proceedings;2023

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