Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables

We study the varying-coefficient partially linear model when some linear covariates are not observed, but their auxiliary instrumental variables are available. Combining the calibrated error-prone covariates and modal regression, we present a two-stage efficient estimation procedure, which is robust...

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Main Authors: Yanting Xiao, Wanying Dong
Format: Article
Language:English
Published: AIMS Press 2023-05-01
Series:AIMS Mathematics
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/math.2023934?viewType=HTML
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author Yanting Xiao
Wanying Dong
author_facet Yanting Xiao
Wanying Dong
author_sort Yanting Xiao
collection DOAJ
description We study the varying-coefficient partially linear model when some linear covariates are not observed, but their auxiliary instrumental variables are available. Combining the calibrated error-prone covariates and modal regression, we present a two-stage efficient estimation procedure, which is robust against outliers or heavy-tail error distributions. Asymptotic properties of the resulting estimators are established. Performance of our proposed estimation procedure is illustrated through some numerous simulations and a real example. And the results confirm that the proposed methods are satisfactory.
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spelling doaj.art-3b5188b418ea407fb635bca25c248b952023-06-12T01:34:03ZengAIMS PressAIMS Mathematics2473-69882023-05-0188183731839110.3934/math.2023934Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variablesYanting Xiao0Wanying Dong1Department of Applied Mathematics, Xi'an University of Technology, Xi'an, Shaanxi 710048, ChinaDepartment of Applied Mathematics, Xi'an University of Technology, Xi'an, Shaanxi 710048, ChinaWe study the varying-coefficient partially linear model when some linear covariates are not observed, but their auxiliary instrumental variables are available. Combining the calibrated error-prone covariates and modal regression, we present a two-stage efficient estimation procedure, which is robust against outliers or heavy-tail error distributions. Asymptotic properties of the resulting estimators are established. Performance of our proposed estimation procedure is illustrated through some numerous simulations and a real example. And the results confirm that the proposed methods are satisfactory.https://www.aimspress.com/article/doi/10.3934/math.2023934?viewType=HTMLvarying-coefficient partially linear modelsauxiliary variableserror-in-variablemodal regression
spellingShingle Yanting Xiao
Wanying Dong
Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables
AIMS Mathematics
varying-coefficient partially linear models
auxiliary variables
error-in-variable
modal regression
title Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables
title_full Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables
title_fullStr Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables
title_full_unstemmed Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables
title_short Robust estimation for varying-coefficient partially linear measurement error model with auxiliary instrumental variables
title_sort robust estimation for varying coefficient partially linear measurement error model with auxiliary instrumental variables
topic varying-coefficient partially linear models
auxiliary variables
error-in-variable
modal regression
url https://www.aimspress.com/article/doi/10.3934/math.2023934?viewType=HTML
work_keys_str_mv AT yantingxiao robustestimationforvaryingcoefficientpartiallylinearmeasurementerrormodelwithauxiliaryinstrumentalvariables
AT wanyingdong robustestimationforvaryingcoefficientpartiallylinearmeasurementerrormodelwithauxiliaryinstrumentalvariables