Nonparametric relative error estimation of the regression function for left truncated and right censored time series data

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.

Automated summary

The paper presents a nonparametric estimator to estimate the regression function of left truncated and right censored data by minimizing the mean squared relative error. The strong uniform convergence of the estimator is established under α-mixing condition with a rate over a compact set. An extensive simulation study is conducted to evaluate the performance of the estimator by comparing its efficiency with that of the classical regression estimator for finite samples across various scenarios. Additionally, a real-world application is provided to demonstrate the practical utility of the proposed estimator.