Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 253 - Innovations in AstroStatistics on Exploring Large Public Data
Type: Invited
Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
Sponsor: Astrostatistics Special Interest Group
Abstract #308028
Title: Handling Model Uncertainty via Smoothed Inference
Author(s): Sara Algeri*
Companies: University of Minnesota
Keywords: mismodeling; smooth tests; smoothed bootstrap; goodness-of-fit; graphical inference

Classical inferential methods often rely on the assumption that one among the models specified under the null or alternative hypothesis provides a suitable representation of the data under study. Unfortunately, when conducting searches for new physics, the specification of a correct model for the data is not always an easy task. Consequently, the validity and the sensitivity of the experiment under study may be substantially compromised. Algeri (2020) introduced a novel statistical approach to perform modeling, estimation, and inference under background mismodeling for large samples in the continuous setting. This work aims to extend the framework proposed in Algeri (2020) to arbitrary large samples from continuous or discrete distributions.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program