Conditional nelson-aalen and kaplan-meier estimators with the müller-wang boundary kernel

Xiaodong Luo, Wei Yann Tsai

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This paper studies the kernel assisted conditional Nelson-Aalen and Kaplan-Meier estimators. The presented results improve the existing ones in two aspects: (1) the asymptotic properties (uniform consistency, rate of convergence and almost sure iid representation) are extended to the entire support of the covariates by use of Müller-Wang boundary kernel; and (2) the order of the remainder terms in the iid representation is improved from (log n/(nhd))3/4 to log n/(nhd) thanks to the exponential inequality for U-statistics of order two. These results are useful for semiparametric estimation based on a first stage nonparametric estimation.

Original languageEnglish
Title of host publicationRecent Advances In Biostatistics
Subtitle of host publicationFalse Discovery Rates, Survival Analysis, And Related Topics
PublisherWorld Scientific Publishing Co.
Number of pages26
ISBN (Electronic)9789814329804
StatePublished - 1 Jan 2011


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