Strong Asymptotic Properties of Kernel Smoothing Estimation for NA Random Variables with Right Censoring
Statistics Theory
2023-02-02 v6 Statistics Theory
Abstract
Most studies for negatively associated (NA) random variables consider the complete-data situation, which is actually a relatively ideal condition in practice. The paper relaxes this condition to the incomplete-data setting and considers kernel smoothing density and hazard function estimation in the presence of right censoring based on the Kaplan-Meier estimator. We establish the strong asymptotic properties for these two estimators to assess their asymptotic behavior and justify their practical use.
Keywords
Cite
@article{arxiv.1901.05764,
title = {Strong Asymptotic Properties of Kernel Smoothing Estimation for NA Random Variables with Right Censoring},
author = {Jianhua Shi and Jiansen Xu and Jinfeng Xu},
journal= {arXiv preprint arXiv:1901.05764},
year = {2023}
}