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Activity Number: 118
Type: Topic Contributed
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #308310
Title: Bayesian Object Regression for Complex, High-Dimensional Data
Author(s): Jeffrey S. Morris*+ and Veera Baladandayuthapani
Companies: The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center
Keywords: Functional Data Analysis ; Image Data ; Object Data ; Manifold Data ; Bayesian Modeling ; Big Data
Abstract:

A growing number of studies yield object data, which involve multiple measurements on some type of structured space, and include, for example, functions, images, shapes, graphs, and trees. The internal structure of the objects can be based on geometry or more complex scientific relationships, and should be accounted for in the modeling. In this talk, I will discuss very general and flexible Bayesian semiparametric modeling frameworks that can be used to perform regression analyses on a broad array of such object data. Our strategy involves the use of various types of basis functions to capture different types of internal structure, using a modeling strategy that is conducive to parallel processing and scales up to very large data sets. The software to apply these methods will be general enough to handle a wide array of models and be used with nearly any type of object data sampled on a fine grid. I will illustrate the flexibility of these methods in several application areas, including event-related potential neuroimaging data, functional MRI data, copy number genomiics data, and opthalmological data involving measurements taken continuously on the surface of the eyeball.


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