Forecasting with Functional and Twice Censored Data

In this study, we propose a new kernel functional regression estimator when the random response variable is subject to twice censoring. Censoring is employed to handle cases where complete response data is unavailable, allowing for more robust and reliable statistical analysis. Our proposed estimato...

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Bibliographic Details
Published inOperations Research Forum Vol. 5; no. 4; p. 108
Main Author Sarra, Leulmi
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.12.2024
Springer Nature B.V
Subjects
Applications of Mathematics
Business and Management
Censorship
Math Applications in Computer Science
Mathematical and Computational Engineering
Nonparametric statistics
Operations Research/Decision Theory
Optimization
Random variables
Regression analysis
Response data analysis
Statistical analysis
Twice censored data
Regression function
Mean square convergence
Functional data
Kernel estimator
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Summary:In this study, we propose a new kernel functional regression estimator when the random response variable is subject to twice censoring. Censoring is employed to handle cases where complete response data is unavailable, allowing for more robust and reliable statistical analysis. Our proposed estimator is specifically designed to provide accurate forecasts even in the presence of such incomplete data. Then, we investigate its mean square convergence, with rate. To reinforce the obtained results, we conduct numerical results to highlight the performance and the accuracy of our proposed estimator.
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ISSN:2662-2556
2662-2556
DOI:10.1007/s43069-024-00390-0