Nonparametric Relative Error Regression for LTRC Data
DOI:
https://doi.org/10.17713/ajs.v54i5.2005Abstract
In the present work, we propose a new kernel estimator of the regression function based on the minimization of the mean squared relative error, when the response variable is subject to both random left truncation and right censoring (LTRC). Such variables typically appear in a medical or an engineering life test studies. Under classical conditions we establish the uniform consistency with a rate and the asymptotic normality for the estimator. The performance of the regression function estimator is evaluated on simulated data sets.
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Copyright (c) 2025 Latifa Adjoudj, Siham Bey, Zohra Guessoum, Abdelkader Tatachak
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