Fall Seminar Series - October 13, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
"Robustifying" Parametric Models via Mixtures of Polya Tree Priors
Tim Hanson
Division of Biostatistics
University of Minnesota
Thursday, October 13, 2005
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
Mixtures of Polya trees models are straightforward to code and
provide a highly flexible alternative when a parametric model may only
hold approximately. In this talk, I provide computational strategies
for
obtaining semiparametric inference for mixtures of finite Polya trees
models given a standard parameterization, including models that would
be
difficult to fit using Dirichlet process mixtures. Recommendations are
put
forth on choosing the level of a finite Polya tree and model comparison
is
discussed. Several examples demonstrate the utility of Polya tree
modeling
including data on bivariate (CD4,CD8) counts fit to a semiparametric
linear mixed model; the classic V.A. lung cancer study data fit to
proportional hazards, proportional odds, and accelerated failure time
models; and serology scores modeled with a stochastic order constraint.