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Activity Number: 587 - Post-Selection Inference
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #300384 Presentation 1 Presentation 2
Title: Inference After Black Box Selection
Author(s): Jelena Markovic*
Companies: Stanford University
Keywords: Selective inference; Data analysis; Model selection; Reproducible research; Confirmatory analysis; Stability selection
Abstract:

We consider the problem of inference for parameters selected to report only after observing the output of some algorithm, the canonical example being inference for model parameters after a model selection procedure. The conditional correction for selection requires knowledge of how the selection is affected by changes in the underlying data, and current research explicitly describes this selection. In this work, we assume 1) we have in silico access to the selection algorithm and 2) for parameters of interest, the data input into the algorithm satisfies (pre-selection) a central limit theorem jointly with an estimator of our parameter of interest. Under these assumptions, we recast the problem into a statistical learning problem which can be fit with off-the-shelf models for binary regression. The feature points in this problem are set by the user, opening up the possibility of active learning methods for computationally expensive selection algorithms. We consider several examples previously out of reach of this conditional approach: stability selection, multiple cross-validation and knockoffs.


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

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