Randomized selective inference


Event details

Date 28.06.2024
Hour 15:1516:15
Speaker Daniel Garcia Rasines, Imperial College London
Category Conferences - Seminars
Event Language English

In contemporary statistical applications, selection of the formal inferential problem is typically done after some level of interaction with the data. Usually, an initial exploratory analysis is used to identify interesting aspects of the population under study, and then the same dataset is used to learn about them. Such “data snooping” invalidates classical inferential procedures. Many approaches have been proposed to ensure inferential validity in these settings.

In this talk, I will present an alternative to data splitting based on randomization which allows for higher selection and inferential power. I will describe the theoretical and empirical advantages of this method and discuss some related problems of current interest.


Practical information

  • Informed public
  • Free


  • Anthony Davison


  • Maroussia Schaffner