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Estimating a Distribution Function for Censored Time Series Data

✍ Scribed by Zongwu Cai

Publisher: Elsevier Science
Year: 2001
Tongue: English
Weight: 159 KB
Volume: 78
Category: Article
ISSN: 0047-259X
DOI: 10.1006/jmva.2000.1953

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✦ Synopsis

Consider a long term study, where a series of dependent and possibly censored failure times is observed. Suppose that the failure times have a common marginal distribution function, but they exhibit a mode of time series structure such as :-mixing. The inference on the marginal distribution function is of interest to us. The main results of this article show that, under some regularity conditions, the Kaplan Meier estimator enjoys uniform consistency with rates, and a stochastic process generated by the Kaplan Meier estimator converges weakly to a certain Gaussian process with a specified covariance structure. Finally, an estimator of the limiting variance of the Kaplan Meier estimator is proposed and its consistency is established.

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