IMA Data Science Seminar: Handling model uncertainties via informative Goodness-of-Fit
The Institute for Mathematics and Its Applications (IMA) Data Science Seminars are a forum for data scientists of IMA academic and industrial partners to discuss and learn about recent developments in the broad area of data science. The seminars take place on Tuesdays from 1:25 p.m. - 2:25 p.m.
This week's speaker, Sara Algeri (University of Minnesota), will be giving a talk titled "Handling model uncertainties via informative Goodness-of-Fit."
Abstract
When searching for signals of new astrophysical phenomena, astrophysicists have to account for several sources of non-random uncertainties which can dramatically compromise the sensitivity of the experiment under study. Among these, model uncertainty arising from background mismodeling is particularly dangerous and can easily lead to highly misleading results. Specifically, overestimating the background distribution in the signal region increases the chances of falsely rejecting the hypothesis that the new source is present. Conversely, underestimating the background outside the signal region leads to an artificially enhanced sensitivity and a higher likelihood of claiming a false discovery. The aim of this work is to provide a self-contained framework to perform modeling, estimation, and inference under background mismodeling. The method proposed allows incorporating the (partial) scientific knowledge available on the background distribution, and provides a data-updated version of it in a purely nonparametric fashion, and thus, without requiring the specification of prior distributions. If a calibration (or control regions) is available, the solution discussed does not require the specification of a model for the signal, however when available, it allows to further improve the accuracy of the analysis and to detect additional and unexpected signal sources.
Biography
Sara Algeri has been an Assistant Professor in the School of Statistics at the University of Minnesota since August 2018. Her appointment at UMN started soon after completing her doctoral studies in statistics at Imperial College London (UK). Her research interests mainly lie in astrostatistics, computational statistics, and statistical inference. The main purpose of her work is to provide generalizable statistical solutions which directly address fundamental scientific questions, and can at the same time be easily applied to any other scientific problem following a similar statistical paradigm. In line with this, motivated by the problem of the detection of particle dark matter, her current research focuses on statistical inference for signal detection under lack of regularity. She is also interested in uncertainty quantification in the context of astrophysical discoveries.