TITLE: Valid Post-Model Selection Inference****

SPEAKER: Professor Linda Zhao

ABSTRACT:

It is common practice in statistical data analysis to perform data-driven
model selection and derive statistical inference from the selected model.
Such inference is generally invalid. We propose to produce valid “post-
selection inference” by reducing the problem to one of simultaneous
inference. We describe the structure of the simultaneous inference problem
and give some asymptotic results. We also develop an algorithm for
numerical computation for the width of our new confidence intervals.

This is joint work with Richard Berk, Lawrence Brown, Andreas Buja, and Kai
Zhang.