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Activity Number:
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669
- New Nonparametric Statistical Methods for High-Dimensional Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #328529
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Presentation
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Title:
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Bayesian Ising Sparse Nonparametric Model
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Author(s):
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Inyoung Kim* and Zaili Fang and Byung-Jun Kim
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Companies:
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Virginia Tech and Virginia Tech and Virginia Polytechnic Inst. & State Univ.
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Keywords:
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Bayesian;
Graphical Model;
Ising;
Sparse
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Abstract:
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In this presentation, we propose a Bayesian Ising Sparse nonparametric model for variable selection approach via the graphical model and Ising model for the ordered categorical clinical outcome. Our 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. Our 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.
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Authors who are presenting talks have a * after their name.
