期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 17, 期 4, 页码 790-808出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/106186008X382683
关键词
Bayesian model selection; Covariance selection; Hyper-inverse Wishart distribution; Lasso; Metropolis algorithm
We describe a serial algorithm called feature-inclusion stochastic search, or FINCS, that uses online estimates of edge-inclusion probabilities to guide Bayesian model determination in Gaussian graphical models. FINCS is compared to MCMC, to Metropolis-based search methods, and to the popular lassos it is found to be superior along a variety of dimensions. leading to better sets of discovered models, greater speed and stability, and reasonable estimates of edge-inclusion probabilities. We illustrate FINCS on an example involving mutual-fund data, where we compare the model-averaged predictive performance of models discovered with FINCS to those discovered by competing methods.
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