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Bayarassou, N.; Hamrani, F.; Ould Saïd, E.

Nonparametric relative error estimation of the regression function for left truncated and right censored time series data. (English) Zbl 07919213

J. Nonparametric Stat. 36, No. 3, 706-729 (2024).
Summary: The paper introduces a nonparametric estimator for the regression function of left truncated and right censored data, achieved through minimising the mean squared relative error. Under \(\alpha\)-mixing condition, strong uniform convergence of the estimator is established with a rate over a compact set. An extensive simulation study is conducted to assess the estimator’s performance, comparing its efficiency to that of the classical regression estimator for finite samples across various scenarios. Moreover, a real world application is presented to demonstrate the practical utility of the proposed estimator.

MSC:

62Gxx Nonparametric inference

Keywords:

kernel estimate; relative error regression; strong mixing condition; strong uniform convergence; truncated-censored data
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References:

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