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. 
Fran\c{c}ois Bachoc, Hannes Leeb, and Benedikt M. P\"otscher
math.ST stat.ME stat.TH