Density estimation with bivariate censored data

• 成果类型: Article
• 署名作者: Wells, MT; Yeo, KP
• 署名单位: National University of Singapore
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291582
• 发表日期: 1996
• 页码: 1566-1574
• 关键词: kaplan-meier estimate; large sample; kernel; regression; bootstrap; plane

摘要

In this article we construct a kernel estimate of the probability density function from bivariate data that have been randomly censored. We study the large-sample properties of the proposed estimator using a strong approximation result. We establish consistency and asymptotic normality and give a convenient representation of the kernel density estimator. Simulation studies show that the proposed procedure gives a good estimate of the true density function even when the sample size is moderate. We discuss various issues about implementation of the estimator, including bandwidth selection and boundary effects. The procedure can be generalized to higher dimensional variables in a straightforward manner.