Nonparametric relative recursive regression estimators for censored data

Abstract

In this paper, we propose a relative recursive regression estimator for censored data defined by the stochastic approximation algorithm to deal with the presence of outliers or when the response is usually positive. We give the central limit theorem and the strong pointwise convergence rate for our proposed nonparametric relative recursive estimators under some mild conditions. We finally developed a second generation plug-in bandwidth selection procedure.

Keywords:

• Bandwidth selection
• relative regression estimation
• censored data
• stochastic approximation algorithm

Acknowledgments

The author would like to thank the Editor-in-Chief of Stochastic Models, Prof. Mark Squillante and two anonymous reviewers for their helpful comments, which helped me to focus on improving the original version of the paper.