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Relative Error Prediction for Twice Censored Data

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Abstract

In this paper we consider the problem of non-parametric relative regression for twice censored data. We introduce and study a new estimate of the regression function when it is appropriate to assess performance in terms of mean squared relative error of prediction. We establish the uniform consistency with rate over a compact set and asymptotic normality of the estimator suitably normalized. The asymptotic variance is explicitly given. A Monte Carlo study is carried out to evaluate the performance of this estimate.

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Acknowledgments

The author would like to thank the editor, the associate editor and the anonymous referees for their valuable comments and suggestions, which led to substantial improvements in the manuscript.

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  1. Lab. de Phys. Math., Fonctions spéciales et Applications (P.M.F.S.A), Hammam-Sousse, Tunisia

    S. Khardani

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  1. S. Khardani
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Correspondence to S. Khardani.

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Khardani, S. Relative Error Prediction for Twice Censored Data. Math. Meth. Stat. 28, 291–306 (2019). https://doi.org/10.3103/S1066530719040045

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