Abstract:

In this talk, a Bayesian variable selection method called SVEN will be described. SVEN is based on a hierarchical Gaussian linear model with priors placed on the regression coefficients as well as on the model space. Embedding a unique model based screening and using fast Cholesky updates, SVEN produces a highly scalable computational framework to explore gigantic model spaces, rapidly identify the regions of high posterior probabilities and make fast inference and prediction. A temperature schedule is used to further mitigate multimodal posterior distributions. The temperature value is guided by our model selection consistency results which hold even when the norm of mean effects solely due to the unimportant variables diverges. An appealing byproduct of SVEN is the construction of novel model weight adjusted prediction intervals. The performance of SVEN will be demonstrated through simulation experiments and a real data example from a genome wide association study with over half a million markers.

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Topic: Colloquia: Department of Statistics and Actuarial Science, The University of Iowa

Time: Apr 1, 2021 03:15 PM Central Time (US and Canada)

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Meeting ID: 949 5289 2803