NONPARAMETRIC BAYESIAN ANALYSIS OF THE COMPOUND POISSON PRIOR FOR SUPPORT BOUNDARY RECOVERY

Given data from a Poisson point process with intensity (x, y) ↦ n₁ (f(x) ≤ y), frequentist properties for the Bayesian reconstruction of the support boundary function f are derived. We mainly study compound Poisson process priors with fixed intensity proving that the posterior contracts with nearly optimal rate for monotone support boundaries and adapts to Hölder smooth boundaries. We then derive a limiting shape result for a compound Poisson process prior and a function space with increasing parameter dimension. It is shown that the marginal posterior of the mean functional performs an automatic bias correction and contracts with a faster rate than the MLE. In this case, (1 – α)-credible sets are also asymptotic (1 – α)-confidence intervals. As a negative result, it is shown that the frequentist coverage of credible sets is lost for linear functions f outside the function class.