A0330
Title: Bayesian Ising sparse nonparametric model
Authors: Inyoung Kim - Virginia Tech (United States) [presenting]
Abstract: A Bayesian Ising Sparse nonparametric model is proposed for variable selection via graphical and Ising models for the ordered categorical clinical outcome. The Bayesian variable problem can be considered as a complete graph and described by an Ising model with random interactions. There are several advantages of our approach: it is applicable to (1) problems with small sample sizes and a larger number of variables and (2) any nonparametric regression models; and easy to (3) incorporate graphical prior information. The results indicate that the best prior for the model coefficients in terms of variable selection should place substantial weight on small, nonzero shrinkage. We also discuss the relationship between the tempering algorithms for the Ising model and the global-local shrinkage approach, showing that the shrinkage parameter plays a tempering role. The methods are illustrated with simulated and real data.
