Bayesian Prediction in Gamma Models: Admissibility and Infinitesimal Prediction

Abstract

We study estimation and prediction in the Gamma model $\mathrm{Ga}(α,β)$, where the shape parameter $α$ is known and the scale parameter $β$ is unknown, under the Kullback--Leibler loss. For $α\le1$, all scale-invariant estimators of $β$ have infinite risk, indicating a qualitative change in the estimation problem at the boundary $α=1$. Our main result is that the Bayesian predictive density based on the Jeffreys prior is admissible for all $α>0$. This resolves the admissibility problem for Bayesian predictive densities in Gamma models. As a related result, we also establish the admissibility of the corresponding Bayesian estimator for $α>1$. To prove the predictive admissibility result, we develop an infinitesimal prediction framework based on Gamma processes. This framework naturally leads to a Kullback--Leibler loss for Lévy densities and establishes a connection between predictive distributions and Lévy measures. Under the resulting loss, the Bayesian predictive Lévy density is shown to be the posterior mean Lévy density. Unlike the normal and Poisson models, infinitesimal prediction in the Gamma model does not reduce to parameter estimation. Instead, it reduces to the estimation of a Lévy density. We relate this phenomenon to mean mixture curvature and discuss it from an information-geometric viewpoint.