Department of Statistical Science
Duke University
presents:
James Berger
Purdue University
"On the Choice of Hyperpriors in Normal Hierarchical Models"
Abstract: Hierarchical modelling is wonderful and here to stay, but we usually "cheat" in choosing the prior distributions for hyperparameters. By "cheating" I mean that we usually choose hyperparameter priors in a casual fashion, often feeling that the choice is not too important. 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.
Finding a solution to this problem is, unfortunately, difficult; indeed, it is not even clear how to attack the problem. In this talk we simply give some illustrations of the problem, and some "solutions" in special cases. Among the topics to be discussed along the way are reference priors for covariance matrices, and propriety and admissibility of priors in exchangeable hierarchical normal models.
Friday, March 8, 1996
11:45 am - 12:45 pm
130 Sociology/Psychology Building Any questions concerning the seminar may be addressed to Cheryl McGhee @[919] 684-8029, e-mail cheryl@stat.duke.edu, or finger seminar@stat.duke.edu