Bayesian Ising Graphical Model for Variable Selection
| Autor: | Z. L. Fang, Inyoung Kim |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Scale (ratio) business.industry Linear model Feature selection Machine learning computer.software_genre 01 natural sciences Nonparametric regression 010104 statistics & probability 0103 physical sciences Linear regression Prior probability Discrete Mathematics and Combinatorics Applied mathematics Ising model Artificial intelligence Graphical model 0101 mathematics Statistics Probability and Uncertainty 010306 general physics business computer Mathematics |
| Zdroj: | Journal of Computational and Graphical Statistics. 25:589-605 |
| ISSN: | 1537-2715 1061-8600 |
| DOI: | 10.1080/10618600.2015.1035438 |
| Popis: | In this article, we propose a new Bayesian variable selection (BVS) approach via the graphical model and the Ising model, which we refer to as the “Bayesian Ising graphical model” (BIGM). The BIGM is developed by showing that the BVS problem based on the linear regression model can be considered as a complete graph and described by an Ising model with random interactions. There are several advantages of our BIGM: it is easy to (i) employ the single-site updating and cluster updating algorithm, both of which are suitable for problems with small sample sizes and a larger number of variables, (ii) extend this approach to nonparametric regression models, and (iii) incorporate graphical prior information. In our BIGM, the interactions are determined by the linear model coefficients, so we systematically study the performance of different scale normal mixture priors for the model coefficients by adopting the global-local shrinkage strategy. Our results indicate that the best prior for the model coefficients in ... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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