Spring 2001 Seminar Series - March 15, 2001
University of Minnesota
School of Statistics
College of Liberal Arts
Modeling Regression Error with a Mixture of Polya Trees
Tim Hanson
Department of Mathematics and Statistics
University of New Mexico
Thursday, March 15, 2001
4:00 PM,
B10
Ford Hall
Minneapolis, East Bank Campus
Social at 3:30 PM,
300
Ford Hall
Abstract
We model the error distribution in the standard linear model as a mixture
of absolutely continuous Polya trees constrained to have median zero. By
considering a mixture, we smooth out the partitioning effects of a simple
Polya tree and the predictiive error density has a derivative everywhere
except zero. The error distribution is centered around a standard
parametric family of distributions and may therefore be viewed as a
generalization of standard models in which important, data-driven features,
such as skewness and multimodality, are allowed. By marginalizing the
Polya trees exact inference is possible up tp MCMC error.