Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data

This study investigates the estimation of the regression function using the kernel method in the presence of missing at random responses, assuming spatial dependence, and complete observation of the functional regressor. We construct the asymptotic properties of the established estimator and derive...

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Main Authors: Fatimah Alshahrani, Ibrahim M. Almanjahie, Tawfik Benchikh, Omar Fetitah, Mohammed Kadi Attouch
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2023/8874880
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author Fatimah Alshahrani
Ibrahim M. Almanjahie
Tawfik Benchikh
Omar Fetitah
Mohammed Kadi Attouch
author_facet Fatimah Alshahrani
Ibrahim M. Almanjahie
Tawfik Benchikh
Omar Fetitah
Mohammed Kadi Attouch
author_sort Fatimah Alshahrani
collection DOAJ
description This study investigates the estimation of the regression function using the kernel method in the presence of missing at random responses, assuming spatial dependence, and complete observation of the functional regressor. We construct the asymptotic properties of the established estimator and derive the probability convergence (with rates) as well as the asymptotic normality of the estimator under certain weak conditions. Simulation studies are then presented to examine and show the performance of our proposed estimator. This is followed by examining a real data set to illustrate the suggested estimator’s efficacy and demonstrate its superiority. The results show that the proposed estimator outperforms existing estimators as the number of missing at random data increases.
format Article
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institution OA Journals
issn 2314-4785
language English
publishDate 2023-01-01
publisher Wiley
record_format Article
series Journal of Mathematics
spelling doaj-art-7be291691c29444c8a90f977718658b02025-08-20T02:06:53ZengWileyJournal of Mathematics2314-47852023-01-01202310.1155/2023/8874880Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial DataFatimah Alshahrani0Ibrahim M. Almanjahie1Tawfik Benchikh2Omar Fetitah3Mohammed Kadi Attouch4Department of Mathematical SciencesDepartment of MathematicsStatistical and Stochastic Process LaboratoryStatistical and Stochastic Process LaboratoryStatistical and Stochastic Process LaboratoryThis study investigates the estimation of the regression function using the kernel method in the presence of missing at random responses, assuming spatial dependence, and complete observation of the functional regressor. We construct the asymptotic properties of the established estimator and derive the probability convergence (with rates) as well as the asymptotic normality of the estimator under certain weak conditions. Simulation studies are then presented to examine and show the performance of our proposed estimator. This is followed by examining a real data set to illustrate the suggested estimator’s efficacy and demonstrate its superiority. The results show that the proposed estimator outperforms existing estimators as the number of missing at random data increases.http://dx.doi.org/10.1155/2023/8874880
spellingShingle Fatimah Alshahrani
Ibrahim M. Almanjahie
Tawfik Benchikh
Omar Fetitah
Mohammed Kadi Attouch
Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data
Journal of Mathematics
title Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data
title_full Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data
title_fullStr Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data
title_full_unstemmed Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data
title_short Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data
title_sort asymptotic normality of nonparametric kernel regression estimation for missing at random functional spatial data
url http://dx.doi.org/10.1155/2023/8874880
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AT tawfikbenchikh asymptoticnormalityofnonparametrickernelregressionestimationformissingatrandomfunctionalspatialdata
AT omarfetitah asymptoticnormalityofnonparametrickernelregressionestimationformissingatrandomfunctionalspatialdata
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