Inference for a constrained parameter in presence of an uncertain constraint
We describe a hierarchical Bayesian approach for inference about a parameter $\theta$ lower-bounded by $\alpha$ with uncertain $\alpha$, derive some basic identities for posterior analysis about $(\theta,\alpha)$, and provide illustrations for normal and Poisson models. For the normal case with unknown mean $\theta$ and known variance $\sigma^2$, we obtain Bayes estimators of $\theta$ that take values on $\mathbb{R}$, but that are equally adapted to a lower-bound constraint in being minimax under squared error loss for the constrained problem.