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Activity Number: 234 - SBSS Student Travel Award Session 2
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #328650
Title: Bayesian Regression with Undirected Network Predictors with an Application to Brain Connectome Data
Author(s): Sharmistha Guha* and Abel Rodriguez
Companies: UC Santa Cruz and UC Santa Cruz
Keywords: Network Predictors; Network shrinkage prior; Node Selection; Edge Selection

This article proposes a Bayesian approach to regression with a continuous scalar response and an undirected network predictor. Network predictor matrices are typically vectorized prior to any analysis, thus failing to account for the important structural information in the network. This results in poor inferential and predictive performance in presence of small sample sizes. We propose a novel class of network shrinkage priors for the coefficient corresponding to the undirected network predictor. The proposed framework is devised to detect both nodes and edges in the network predictive of the response. Empirical results in simulation studies illustrate strikingly superior inferential and predictive gains of the proposed framework in comparison with the ordinary high dimensional Bayesian shrinkage priors and penalized optimization schemes. We apply our method to a brain connectome dataset that contains information on brain networks along with a measure of creativity for multiple individuals.

Authors who are presenting talks have a * after their name.

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