Recent Advances in Post-Selection Statistical Inference
Robert Tibshirani, Professor of Biomedical Data Science, and Statistics Stanford University
In this era of big data and complex statistical modeling, scientists use sophisticated computational tools to search through a large number of models, looking for meaningful patterns. The challenge is then to judge the strength of a large number of apparent associations that have been found. This statistical problem has become known as “Post-selection inference,” the assessment of significance and effect sizes from a data-set after mining the same data to find these associations. In this talk I will discuss new methods for computing p-values and confidence intervals in regression, that correctly account for the adaptive selection of the model. This is joint work with Jonathan Taylor, Ryan Tibshirani, Will Fithian and Richard Lockhart.