Relative error prediction: Strong uniform consistency for censoring time series model

This article considers an adaptive method based on the relative error criteria to estimate the regression operator by a kernel smoothing. It is assumed that the variable of interest is subject to random right censoring and that the observations are from a stationary α-mixing process. The uniform alm...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2023-06, Vol.52 (11), p.3709-3729
Main Authors: Bouhadjera, Feriel, Elias, Ould Saïd, Mohamed Riad, Remita
Format: Article
Language:English
Subjects:
Censored data
Consistency
Errors
Kernel estimate
Mathematics
Outliers (statistics)
regression function
relative error
Statistics
strong mixing
uniform almost sure consistency
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Summary:This article considers an adaptive method based on the relative error criteria to estimate the regression operator by a kernel smoothing. It is assumed that the variable of interest is subject to random right censoring and that the observations are from a stationary α-mixing process. The uniform almost sure consistency over a compact set with rate where we highlighted the covariance term is established. The simulation study indicates that the proposed approach has better performance in the presence of high level censoring and outliers in data to an existing classical method based on the least squares. An experiment prediction shows the quality of the relative error predictor.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2021.1979584