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arXiv · 1901.05764

Strong Asymptotic Properties of Kernel Smoothing Estimation for NA Random Variables with Right Censoring

Jianhua Shi·Jiansen Xu·Jinfeng Xu
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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.

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Jianhua Shi, Jiansen Xu, Jinfeng Xu. 2019-01-17. Strong Asymptotic Properties of Kernel Smoothing Estimation for NA Random Variables with Right Censoring. https://arxiv.org/abs/1901.05764

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