In some long term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common marginal distribution function having a density, and the nonparametric estimation of density and hazard rate under random censorship is of our interest.
Density and hazard rate estimation for censored data via strong representation of the Kaplan-Meier estimator
β Scribed by S. H. Lo; Y. P. Mack; J. L. Wang
- Publisher
- Springer
- Year
- 1989
- Tongue
- English
- Weight
- 581 KB
- Volume
- 80
- Category
- Article
- ISSN
- 1432-2064
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π SIMILAR VOLUMES
A strong i.i.d. representation is obtained for the product-limit estimator of the survival function based on left truncated and right censored data. This extends the result of Chao and Lo (1988, Ann. Statist. 16, 661-668) for truncated data. An improved rate of the approximation is also obtained on
In this paper we consider the TJW product-limit estimator F n (x) of an unknown distribution function F when the data are subject to random left truncation and right censorship. An almost sure representation of PL-estimator F n (x) is derived with an improved error bound under some weaker assumption