The Department of Statistics and Actuarial Science Spring Colloquium Series presents:

Daniel Sewell, Professor of Biostatistics, College of Public Health, University of Iowa

"Relational Priors"

Abstract: It is common to hold prior beliefs that are not characterized by points in the parameter space but instead are relational in nature and can be described by a linear subspace. While some previous work has been done to account for such prior beliefs, the focus has primarily been on point estimators within a regression framework. We argue, however, that prior beliefs about parameters ought to be encoded into the prior distribution rather than in the formation of a point estimator. In this way, the prior beliefs help shape all inference. Through exponential tilting, we propose a fully generalizable method of taking existing prior information from, e.g., a pilot study, and combining it with additional prior beliefs represented by parameters lying on a linear subspace. We provide computationally efficient algorithms for posterior inference that, once inference is made using a non-tilted prior, does not depend on the sample size. Finally, an extension of this method towards convex subspaces is provided along with theoretical results.

Meet and Greet at 3:00 pm in 241 SH. Colloquium at 3:30 pm in 61 SH.