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

2019/01/17 by Jianhua Shi, Shi, Jianhua, Jiansen Xu +3
Mathematics · #FOS: Mathematics #Statistical Distribution Estimation and Applications #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1901.05764

openalex publication_date 2019/01/17 · openalex created_date 2023/02/13 · openalex updated_date 2026/07/28

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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