Estimation of Survival Curves from Dependent Censorship Models via a Generalized Self-Consistent Property with Nonparametric Bayesian Estimation Application
Year: 1986
Type: article
Abstract: This article presents a family of estimators of the survival function based on right-censored observations which admit the possibility that the censoring variables may not be independent of the true failure variables. This family is obtained by generalizing the self-consistent property (Efron, 1967) of the product limit estimator (Kaplan and Meier, 1958). By assuming a Dirichlet process prior distribution of the observable random vectors, nonparametric Bayesian estimators of the survival curve-which is also a member of this family--are derived under a special loss function. These nonparametric Bayesian estimators generalize results of Susarla and Van Ryzin (1976), who impose a Dirichlet process prior on the failure survival function without considering any prior distribution of the censoring variables. Large sample properties of this family of nonparametric Bayesian estimators are also derived.
Source: The Annals of Statistics
Author: Wei-Yann Tsai
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2064657706