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The k nearest neighbors local linear estimator of semi functional partial linear model with missing response at random

dc.contributor.authorAmina Naceri
dc.contributor.authorTawfik Benchikh
dc.contributor.authorIbrahim M. Almanjahie
dc.contributor.authorOmar Fetitah
dc.contributor.authorMohammed Kadi Attouch
dc.contributor.authorFatimah Alshahrani
dc.contributor.otherLaboratory of Statistics and Stochastic Processes, University of Djillali Liabes BP 89, Sidi Bel Abbes 22000, Algeria; Email: benchikh.tawfik@gmail.com, attou_kadi@yahoo.fr
dc.contributor.otherLaboratory of Statistics and Stochastic Processes, University of Djillali Liabes BP 89, Sidi Bel Abbes 22000, Algeria; Email: benchikh.tawfik@gmail.com, attou_kadi@yahoo.fr
dc.contributor.otherDepartment of Mathematics, College of Science, King Khalid University, Abha 62223, Saudi Arabia; Email: imalmanjahi@kku.edu.sa
dc.contributor.otherLaboratory of Statistics and Stochastic Processes, University of Djillali Liabes BP 89, Sidi Bel Abbes 22000, Algeria; Email: benchikh.tawfik@gmail.com, attou_kadi@yahoo.fr
dc.contributor.otherLaboratory of Statistics and Stochastic Processes, University of Djillali Liabes BP 89, Sidi Bel Abbes 22000, Algeria; Email: benchikh.tawfik@gmail.com, attou_kadi@yahoo.fr
dc.contributor.otherDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; Email: fmalshahrani@pnu.edu.sa
dc.date.accessioned2025-08-27T02:32:32Z
dc.date.accessioned2025-10-08T08:06:46Z
dc.date.available2025-10-08T08:06:46Z
dc.date.issued2025-07
dc.identifier.urihttps://www.aimspress.com/article/doi/10.3934/math.2025714
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/35591
dc.description.abstractThis paper aims to investigate a semi-functional partial linear regression model in the presence of missing data in the response variable under the missing at random mechanism. We construct estimators using the kNN-local linear method and establish the asymptotic distribution of the parametric component. Additionally, the uniform almost complete consistency rates for the nonparametric component with respect to the number of neighbors under appropriate conditions is derived. Through simulations and real data analysis, we assess the effectiveness of the proposed approach and demonstrate its superiority by comparing it with existing methods for semi-functional partial linear regression models.
dc.language.isoEN
dc.publisherAIMS Press
dc.subject.lccMathematics
dc.titleThe k nearest neighbors local linear estimator of semi functional partial linear model with missing response at random
dc.typeArticle
dc.description.keywordsfunctional data analysis
dc.description.keywordspartial linear regression
dc.description.keywordsmissing at random data
dc.description.keywordsknn estimation
dc.description.keywordslocal linear estimation
dc.description.pages15929-15954
dc.description.doi10.3934/math.2025714
dc.title.journalAIMS Mathematics
dc.identifier.e-issn2473-6988
dc.identifier.oai83345cbd9e86447d8b84516f28535c5e
dc.journal.infoVolume 10, Issue 7


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