Nonparametric Regression with Randomly Right-Censored Data

Abstract: The purpose of this study is to estimate the right-censored nonparametric model with kernel smoothing method. To consider the censorship, we used Kaplan-Meier estimator proposed by Stute (1993). In nonparametric statistics, a kernel smoothing method needs a smoothing parameter which is also called as a bandwidth parameter. In this study, we choose the bandwidth parameter by using three selection methods such as improved version of Akaike information criterion (AICc), Risk estimation using classical pilots (RECP) and Generalized cross-validation(GCV) method, respectively. For this purpose, a Monte-Carlo simulation study is performed to illustrate which selection criterion gives the best estimation for different sample sizes and censoring levels.