PLACE: 171 Durham

SPEAKER:
Luis Raul Pericchi
Universidad Simon Bolvar Caracas

TITLE:
Objective Bayes Factors and Posterior Model Probabilities: A Host of
Potential Applications

ABSTRACT:

In recent years, there have been developments within the Bayesian approach to
statistics, that have permitted solving ever more complex problems with milder
prior assumptions. The conjunction of these two directions has certainly
enabled Bayesian statistics to share a wider market of the practice of
statistics.  One fundamental area of statistics falls under the term of model
comparisons, hypothesis testing and inferences under model uncertainty, an area
in which, arguably, Bayesian methods have much to contribute to improve the
practice of statistics.  In this area, objective and intrinsic Bayes factors,
intrinsic priors and related methods, are studied in Berger and Pericchi (1996,
2000), who show how to produce Bayes Factors and model posterior probabilities
with minimal prior inputs.  This new theory has attracted attention, and is now
used and studied by both practitioners and theoreticians of statistics. In this
talk we briefly present the theory and introduce applications to automatic
robust statistical inference and to model comparisons of dynamic linear models.
There are many other potential applications that I hope to discuss during my
visit to Iowa State University.

Key Words:  Intrinsic Bayes factors and priors; objective Bayes
factors;                              principle of model parsimony, posterior
model probabilities
 

COFFEE: 3:40 p.m., 104 Snedecor