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

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  • Feriel Bouhadjera
  • Ould Saïd Elias
  • Remita Mohamed Riad

Abstract

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.

Suggested Citation

  • Feriel Bouhadjera & Ould Saïd Elias & Remita Mohamed Riad, 2023. "Relative error prediction: Strong uniform consistency for censoring time series model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(11), pages 3709-3729, June.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:11:p:3709-3729
    DOI: 10.1080/03610926.2021.1979584
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