Version 1
: Received: 5 December 2023 / Approved: 7 December 2023 / Online: 7 December 2023 (09:14:16 CET)
How to cite:
Benchikh, T.; Almanjahie, I.M.; Fetitah, O.; Attouch, M.K. Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random. Preprints2023, 2023120496. https://doi.org/10.20944/preprints202312.0496.v1
Benchikh, T.; Almanjahie, I.M.; Fetitah, O.; Attouch, M.K. Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random. Preprints 2023, 2023120496. https://doi.org/10.20944/preprints202312.0496.v1
Benchikh, T.; Almanjahie, I.M.; Fetitah, O.; Attouch, M.K. Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random. Preprints2023, 2023120496. https://doi.org/10.20944/preprints202312.0496.v1
APA Style
Benchikh, T., Almanjahie, I.M., Fetitah, O., & Attouch, M.K. (2023). Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random. Preprints. https://doi.org/10.20944/preprints202312.0496.v1
Chicago/Turabian Style
Benchikh, T., Omar Fetitah and Mohammed kadi Attouch. 2023 "Nonparametric Partial Linear Estimation for Spatial Functional Data with Missing At-Random" Preprints. https://doi.org/10.20944/preprints202312.0496.v1
Abstract
The aim of this paper is to study a semi-functional partial linear regression model (SFPLR) for spatial data with responses missing at random. The estimators are constructed by the kernel method, and some asymptotic properties such as probability convergence rates of the nonparametric component and asymptotic distribution of the parametric and nonparametric components are established under certain conditions. Next, the performances and the superiority of these estimators are presented and examined using a study on simulated data and on real data by carrying out a comparison between our semi-functional partially linear model with MAR estimator (SFPLRM), the semi-functional partially linear model with the full-case estimator (SFPLRC) and the nonparametric functional model estimator with MAR (FNPM). The results show that the proposed estimators outperform existing estimators as the number of random missing data increases.
Keywords
Missing at random data; Functional data analysis; Asymptotic normality; spatial data; Kernel regression method
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
