Valid confidence intervals for post-model-selection predictors


We consider inference post-model-selection in linear regression. In this
setting, Berk et al.(2013) recently introduced a class of confidence sets, the
so-called PoSI intervals, that cover a certain non-standard quantity of interest
with a user-specified minimal coverage probability, irrespective of the model
selection procedure that is being used. In this paper, we generalize the PoSI
intervals to post-model-selection predictors.

This is a joint work with Hannes Leeb and Benedikt Poetscher.

A full version of this work can be found
at https://arxiv.org/abs/1412.4605