SPEAKER: Jonathan Taylor, Professor, Statistics, Stanford University
TITLE: "Inference after selection through a black box"
ABSTRACT: We consider the problem of inference for parameters selected for reporting only after 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 much current research describes this selection explicitly. In this work, we assume 1) we have have access, in silico, to the selection algorithm itself 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. We consider two examples previously out of reach of this conditional approach: stability selection and inference after multiple runs of Model-X knockoffs.
This is joint work with Jelena Markovic and Jeremy Taylor.
Speaker's web page: http://statweb.stanford.edu/~jtaylo/
DATE: Thursday, February 7th, 4:10pm
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS: 3:30pm MSB 4110 (4th floor lounge)
STA 290 Seminar List: https://statistics.ucdavis.edu/seminars