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

Jian-hua Shi, Jian-sen Xu and Jin-feng Xu

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 12, 4531-4541

Abstract: 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.

Date: 2024
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DOI: 10.1080/03610926.2023.2184189

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