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Activity Number: 465
Type: Invited
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #314681
Title: Inference Conditional on Model Selection with a Focus on Procedures Characterized by Quadratic Inequalities
Author(s): Joshua Loftus*
Companies: Stanford University
Keywords: model selection ; conditional inference ; selective inference
Abstract:

Beginning with Lockhart et al. (2014), a surge of recent research has focused on inference after model selection using a framework of conditioning on the selected model. We outline a general procedure that describes model selection in terms of quadratic inequalities and illustrate the utility of this framework with several examples including group lasso, forward stepwise regression, and k-means clustering. Time permitting, we will discuss the interpretation and optimality properties of these conditional inference techniques described in Fithian et al. (2014), and some recent headway in the case where the error variance is unknown. Taken together, this line of inquiry is yielding a rigorous and interpretable new paradigm for post-selection inference.


Authors who are presenting talks have a * after their name.

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