Nonparametric estimation of hazard quantile function

Abstract

In this paper, we study the estimation of the hazard quantile function based on right censored data. Two nonparametric estimators, one based on the empirical quantile density function and the other using the kernel smoothing method, are proposed. Asymptotic properties of the kernel-based estimator are discussed. Monte Carlo simulation studies are conducted to compare the two estimators. The method is illustrated for a real data set.

Keywords:

• nonparametric estimator
• hazard quantile function
• kernel smoothing

2000 Mathematical Subject Classification :

• 62N05

Acknowledgements

This research work is supported by the University Grants Commission, Government of India.