Estimating a Distribution Function for Censored Time Series Data

Zongwu Cai ()

Journal of Multivariate Analysis, 2001, vol. 78, issue 2, 299-318

Abstract: 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 [alpha]-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.

Keywords: [alpha]-mixing; censored; data; consistency; Kaplan-Meier; estimator; time; series; analysis; variance; estimator; weak; convergence (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (3)

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