Nonparametric synthetic data regression estimation for censored survival data

Abstract

This paper studies the large sample properties of the censored data nonparametric regression estimation in multivariate case. The data transformation is derived from the synthetic data method proposed by Leurgans. The asymptotic distributions are derived by counting process techniques. The results are then considered as an extension of the kernel smoothing for the complete data.