Valid Post-Selection Inference

Title : Valid Post-Selection Inference

Time : 2015/05/21(星期四), 16:00-17:00

Location : 伟清楼209（统计学研究中心会议室）

Speaker: Kai Zhang, UNC-Chapel Hill

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. Simultaneity is required for all linear functions that arise as coefficient estimates in all submodels. By purchasing “simultaneity insurance” for all possible submodels, the resulting post-selection inference is rendered universally valid under all possible model selection procedures. This inference is therefore generally conservative for particular selection procedures, but it is always less conservative than full Scheffe protection. Importantly it does not depend on the truth of the selected submodel, and hence it produces valid inference even in wrong models. We describe the structure of the simultaneous inference problem and give some asymptotic results.

About the speaker:
Dr. Kai Zhang is an Assistant Professor in the Department of Statistics and Operation Research, University of North Carolina at Chapel Hill. He received his Ph.D. in Mathematics from Temple University (2007) and Statistics from the Wharton School, University of Pennsylvania (2012). He has been awarded several honors, including the Laha Travel Award by the Institute of Mathematical Statistics in 2011. He has published many research papers in mainstream statistics and physics journals, including the Annals of Statistics, Journal of the American Statistical Association and so on. He has served as referee for the Annals of Statistics, Journal of Quantitative Analysis in Sports and Physica A: Statistical Mechanics and its Applications.