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DSS: Decoupled Shrinkage and Selection in linear models

Event Type
Statistics Department
165 Everitt Lab
Apr 4, 2013   4:00 - 5:00 pm  
Richard Hahn (University of Chicago Booth School of Business)

We propose a new variable selection approach from a fully Bayesian decision theory viewpoint. The method draws an explicit distinction between actions and inferences, effectively dealing with the trade-off associated with the competing goals of predictive generalization and interpretability. By decoupling posterior learning from model reporting, our approach creates a flexible framework where continuous shrinkage priors can be used but ``sparse solutions'' can be obtained. The method generalizes straightforwardly to the GLM setting.

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