Strong asymptotic properties of kernel smoothing estimation for NA random variables with right censoring

Most studies for negatively associated (NA) random variables consider the complete-data situation, which is actually a relatively ideal condition in practice. The article relaxes this condition to the incomplete-data setting and considers kernel smoothing density and hazard function estimation in th...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2024-06, Vol.53 (12), p.4531-4541
Main Authors: Shi, Jian-hua, Xu, Jian-sen, Xu, Jin-feng
Format: Article
Language:English
Subjects:
Asymptotic properties
Kaplan-Meier estimator
kernel smoothing estimator
NA random variable
Random variables
right censoring
Smoothing
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Summary:Most studies for negatively associated (NA) random variables consider the complete-data situation, which is actually a relatively ideal condition in practice. The article 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.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2023.2184189