  In this paper we introduce the subdistribution beta-Stacy process, a novel
Bayesian nonparametric process prior for subdistribution functions useful for
the analysis of competing risks data. In particular, we i) characterize this
process from a predictive perspective by means of an urn model with
reinforcement, ii) show that it is conjugate with respect to right-censored
data, and iii) highlight its relations with other prior processes for competing
risks data. Additionally, we consider the subdistribution beta-Stacy process
prior in a nonparametric regression model for competing risks data which,
contrary to most others available in the literature, is not based on the
proportional hazards assumption.
