Bayesian bivariate survival estimation

Abstract

There is no easy extension of the Kaplan-Meier and Nelson-Aalen estimators to the bivariate case, and estimating bivariate survival distributions nonparametrically is associated with various nontrivial problems. The Dabrowska estimator will, for example, associate negative mass to some subsets. Bayesian methods hold some promise as they will avoid the negative mass problem, but are also prone to difficulties. We simplify and extend an example by Pruitt to show that the posterior distribution from a Dirichlet process prior is inconsistent. We construct a different nonparametric prior via Beta processes and provide an updating scheme that utilizes only the most relevant parts of the likelihood, and show that this leads to a consistent estimator.