A Bayesian nonparametric goodness of fit test for right censored data based on approximate samples from the beta-Stacy process

In recent years, Bayesian nonparametric statistics has received extraordinary attention. The beta-Stacy process, a generalization of the Dirichlet process, is a fundamental tool in studying Bayesian nonparametric statistics. In this article, we derive a simple, yet efficient, way to simulate the beta-Stacy process. We compare the efficiency of the new approximation to several other well-known approximations, and we demonstrate a significant improvement. Using the Kolmogorov distance and samples from the beta-Stacy process, a Bayesian nonparametric goodness of fit test is proposed. The proposed test is very general in the sense that it can be applied to censored and non-censored observations. Some illustrative examples are included.