Title: Valid confidence intervals for post-model-selection predictors
Authors: Francois Bachoc - Universite Paul Sabatier (France) [presenting]
Hannes Leeb - University of Vienna (Austria)
Benedikt Poetscher - University of Vienna (Austria)
Abstract: Inference post-model-selection in linear regression is considered. In this setting, it has been 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. We generalize the PoSI intervals to confidence intervals for post-model-selection predictors.