Speaker: Aaron Porter (University of Missouri)
Title: Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models
Abstract: We introduce a general Bayesian hierarchical framework that incorporates a flexible nonparametric data model specification through the use of empirical likelihood methodology, which we term semiparametric hierarchical empirical likelihood (SHEL) models. Although general dependence structures can be readily accommodated, we focus on spatial modeling, a relatively underdeveloped area in the empirical likelihood literature. Importantly, the models we develop naturally accommodate spatial association on irregular lattices and irregularly spaced point referenced data. We illustrate the methodology using a spatial SHEL Fay-Herriot model and apply it demographic data from the American Community Survey. We demonstrate the superior performance of our model, in terms of mean squared prediction error, over standard parametric analyses. (archived presentation)
This is joint work with Scott H. Holan and Christopher K. Wikle
Location:
º Carnegie Mellon: contact William Eddy (bill@cmu.edu)
º Census Bureau headquarters: Conference Room 1, contact Ron Jarmin (ron.s.jarmin@census.gov)
º Cornell University, Ithaca campus: Ives 381
º Duke University: contact Alan Karr (karr@niss.org)
º University of Michigan: Room 368 ISR-Thompson, contact Maggie Levenstein (maggiel@umich.edu)
º University of Missouri: contact Scott Holan (holans@missouri.edu)
º University of Nebraska-Lincoln: Room TBD: contact: Allan McCutcheon (amccutcheon1@unl.edu)
º Northwestern University: contact Zach Seeskin (z-seeskin@u.northwestern.edu)
- Live video conference. Please contact Lars Vilhuber (lars.vilhuber@cornell.edu) if you wish to participate by video conference, by Monday, May 5, 2014.
