Density Estimation with Bivariate Censored Data
作者:
MartinT. Wells,
KweePoo Yeo,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 436
页码: 1566-1574
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476724
出版商: Taylor & Francis Group
关键词: Bivariate failure time data;Kernel density estimation;Strong approximation;Survival analysis
数据来源: Taylor
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.
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