Name: PHYSTAT Meeting, dedicated to the memory of Sir David Cox
Start: 2022-03-23T16:00:00+01:00
End: 2022-03-23T18:00:00+01:00
Location: No location set

- 1
  
  Short contributions from Louis Lyons, Heather Battey, Brad Efron, Bob Cousins, and Nancy Reid
  
  cousins_DRCox_at_PhyStat.pdf
- 2
  
  PHYSTAT seminar: Computationally efficient methods for testing multivariate distributions with unknown parameters
  
  Abstract: Despite he popularity of classical goodness fit tests such as Pearson’s chi-squared and Kolmogorov-Smirnov, their applicability often faces serious challenges in practical applications. For instance, in a binned data regime, low counts may affect the validity of the asymptotic results. Excessively large bins, on the other hand, may lead to loss of power. In the unbinned data regime, tests such as Kolmogorov-Smirnov and Cramer-von Mises do not enjoy distribution-freeness if the models under study are multivariate and/or involve unknown parameters. As a result, one needs to simulate the distribution of the test statistic on a case-by-case basis. In this talk, I will discuss a testing strategy that allows us to overcome these shortcomings and equips experimentalists with a novel tool to perform goodness-of-fit while reducing substantially the computational costs.
  
  Prof. Sara Algeri is a Statistician at the University of Minnesota. Her interests include statistical inference, non-parametrics and computational statistics. For the fields of Astrophysics and Particle Physics, the topics she lists are signal detection, background estimation, and uncertainty quantification. She has participated at several PHYSTAT Workshops, and gave the statistician's summary talk at the recent PHYSTAT-Systematics.
  
  Speaker: Sara Algeri (University of Minnesota)
  
  Phystat_seminar0316.pdf
- 17:40
  
  Discussion time