Nonparametric priors for vectors of survival functions

Abstract

The paper proposes a new nonparametric prior for two-dimensional vectors of survival functions (S1,S2). The definition we introduce is based on the notion of Lévy copula and it will be used to model, in a nonparametric Bayesian framework, two-sample survival data. Such an application will yield a natural extension of the more familiar neutral to the right process of Doksum (1974) adopted for drawing inferences on single survival functions. We, then, obtain a description of the posterior distribution of (S1,S2), conditionally on possibly right-censored data. As a by-product of our analysis, we find out that the marginal distribution of a pair of observations from the two samples coincides with the Marshall-Olkin or the Weibull distribution according to specific choices of the marginal Lévy measures.Bayesian nonparametrics; Completely random measures; Dependent stable processes; Lévy copulas; Posterior distribution; Right-censored data; Survival function