Posterior propriety and admissibility of hyperpriors in normal hierarchical models

James O. Berger, William Strawderman, Dejun Tang
2005 Annals of Statistics  
Hierarchical modeling is wonderful and here to stay, but hyperparameter priors are often chosen in a casual fashion. Unfortunately, as the number of hyperparameters grows, the effects of casual choices can multiply, leading to considerably inferior performance. As an extreme, but not uncommon, example use of the wrong hyperparameter priors can even lead to impropriety of the posterior. For exchangeable hierarchical multivariate normal models, we first determine when a standard class of
more » ... d class of hierarchical priors results in proper or improper posteriors. We next determine which elements of this class lead to admissible estimators of the mean under quadratic loss; such considerations provide one useful guideline for choice among hierarchical priors. Finally, computational issues with the resulting posterior distributions are addressed.
doi:10.1214/009053605000000075 fatcat:q4kgbyzzubbjho7ujb2v2me2zi