Weighted local polynomial estimations of a non-parametric function with censoring indicators missing at random and their applications

Abstract

In this paper, we consider the weighted local polynomial calibration estimation and imputation estimation of a non-parametric function when the data are right censored and the censoring indicators are missing at random, and establish the asymptotic normality of these estimators. As their applications, we derive the weighted local linear calibration estimators and imputation estimations of the conditional distribution function, the conditional density function and the conditional quantile function, and investigate the asymptotic normality of these estimators. Finally, the simulation studies are conducted to illustrate the finite sample performance of the estimators.

Keywords

Local polynomial estimation / asymptotic normality / non-parametric function / censoring indicator / missing at random

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