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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.
