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                                    “PHYSTAT Dark Matter 2019”

                                               https://indico.cern.ch/event/769726/

 

 

                                                    DAY-1,  31.07.2019

 

 -  0930-1050: Glen Cowan, Louis Lyons

    Session: Preamble and Intro to Statistics

    Convener: Jan Conrad

 

                                              “Introductory Statistics Talks”

 

        Date:

    Download-files:

      Time:

 Wednesday, 31. July 2019

    Video-Recording for any system with MP4-support

   - Video1.mp4  (ca.296 Mb)

 09:30 – 10:20

 

                                    

 

 

        Date:

    Download-files:

      Time:

 Wednesday, 31. July 2019

    Video-Recording for any system with MP4-support

   - Video2.mp4  (ca.275 Mb)

 10:25 – 11:15

 

                                    

Description:

These will review some simple statistical concepts that are relevant to this Workshop.

Among other topics, it will include upper limits, p-values and likelihood ratios.

It is intended for those who would like to be reminded of their Statistics,

before the Workshop begins.

 

Glen Cowan

Louis Lyons (Imperial College (GB))

 

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 - 1510-1540: Sara Algeri.

    Session: Blind Analysis, Look Elsewhere

 

             "Correcting for the look-elsewhere effect: why, when and how"

 

        Date:

    Download-files:

      Time:

 Wednesday, 31. July 2019

    Video-Recording for any system with MP4-support

   - Video3.mp4  (ca.167 Mb)

 15:30 – 16:00

 

                                    

Description:

The look-elsewhere effect is a phenomenon which often arises when looking for signals

whose location is not known in advance. In this setting, signal searches can be conducted

by performing several tests of hypothesis at different positions over the search area

considered. However, if the result of each individual test is not adequately adjusted for the

fact that many tests are conducted simultaneously, the overall probability of false

discoveries rapidly increases with the number of tests. Alternatively, one can consider the

unknown position of the signal as a nuisance parameter and construct confidence intervals

and statistical tests of hypothesis by means of Monte Carlo simulations or methods relying

on random fields and extreme value theory. The goal of this talk is to provide an overview

of the most common methods used to correct for the look-elsewhere effect and highlight

their advantages and limitations with respect to the goal of the experiment and the conditions

under which the statistical analysis is performed.

 

Dr. Sara Algeri (University of Minnesota)

 

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                                                    DAY-2,  01.08.2019

 

 - 0900-09.30 Alessandra Brazzale

    Session: Limit Setting Methods

    Convener: Tarek Saab

 

                                      "Likelihood asymptotics and beyond"

 

        Date:

    Download-files:

      Time:

 Thursday, 01. Aug. 2019

    Video-Recording for any system with MP4-support

   - Video1.mp4  (ca.209 Mb)

 09:00 – 09:30

 

                                    

Description:

I will review some classical methods of asymptotic inference and their higher order

extensions. The focus will be on modern likelihood based solutions, though Bayesian

counterparts will be mentioned in by-passing. The discussion will touch upon topics

such as small sample sizes, large number of nuisance parameters, nonregular settings

and complex models.

 

Alessandra Brazzale

 

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 - 11:00-1135 Sara Algeri

   Session: Signal and Backgrounds

 

                      "Detecting new signals under background mismodelling"

 

        Date:

    Download-files:

      Time:

 Thursday, 01. Aug. 2019

    Video-Recording for any system with MP4-support

   - Video2.mp4  (ca.184 Mb)

 11:00 – 11:35

 

                                    

Description:

When searching for new astrophysical phenomena, uncertainty arising from background

mismodelling can dramatically compromise the sensitivity of the experiment under study.

Specifically, overestimating the background distribution in the signal region increases the

chances of missing new physics. Conversely, underestimating the background outside

the signal region leads to an artificially enhanced sensitivity and a higher likelihood of

claiming false discoveries. The aim of this work is to provide a unified statistical algorithm

to perform modelling, estimation, inference and signal characterization under background

mismodelling. The method proposed allows to incorporate the (partial) scientific knowledge

available on the background distribution, and provides a data-updated version of it in a

purely nonparametric fashion, without requiring the specification of prior distributions.

If a calibration sample or control regions are available, the solution discussed does not

require the specification of a model for the signal; however, if the signal distribution is known,

it allows to further improve the accuracy of the analysis and to detect additional signals

of unexpected new sources.

 

Dr. Sara Algeri (University of Minnesota)

 

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                                                    DAY-3,  02.08.2019

 

 - 1330-1400: Robert Cousins

   Presenters: : Robert Cousins Jr;

                        Robert Cousins Jr;

                        Robert Dacey Cousins Jr

 

"Reflections on 20+ years of Feldman-Cousins: Hypothesis testing of a point null

  vs a continuous alternative"

 

        Date:

    Download-files:

      Time:

  Friday, 02. Aug. 2019

    Video-Recording for any system with MP4-support

   - Video1.mp4  (ca.246 Mb)

 13:30 – 14:20

 

                                    

Description:

I will discuss aspects of the frequentist and Bayesian approaches

to testing a point null hypothesis (say mu=0) versus a continuous

alternative hypothesis (say mu>0). This test arises frequently

in particle physics (including dark matter searches), where

mu is the signal strength. The frequentist testing approach

maps identically onto the frequentist theory of confidence

intervals. Thus, as Feldman and Cousins eventually realized,

the method advocated in their 1998 paper on confidence intervals

maps identically onto the "classical" theory of hypothesis

testing in Kendall and Stuart (which in addition includes

nuisance parameters). Meanwhile, the traditional Bayesian

approach to hypothesis testing (due to Jeffreys) is completely

separate from the Bayesian approach to credible intervals, with

no corresponding mapping. Direct sensitivity to the prior pdf

for mu, even in the asymptotic limit of large sample size,

is a consequence, as is the Jeffreys-Lindley paradox

(arXiv:1310.3791).

My talk will draw on parts of my “Lectures on Statistics in

Theory: Prelude to Statistics in Practice” arXiv:1807.05996.

 

Robert Cousins Jr (University of California Los Angeles (US))

Robert Cousins Jr (University of California Los Angeles (US))

Robert Dacey Cousins Jr (Univ. of California Los Angeles (UCLA))

 

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Session: White Paper/Discussion 3

 

 - 1540-1640: Brazzale, Lippincott

    Session: Optional: White Paper 2

 

                                                    "Statisticians summary"

 

        Date:

    Download-files:

      Time:

  Friday, 02. Aug. 2019

    Video-Recording for any system with MP4-support

   - Video2.mp4  (ca.183 Mb)

 15:30 – 16:10

 

                                    

Description:

Statisticians summary

Primary author: Alessandra Brazzale

 

                                                     "Physicists Summary"

 

        Date:

    Download-files:

      Time:

  Friday, 02. Aug. 2019

    Video-Recording for any system with MP4-support

   - Video3.mp4  (ca.130 Mb)

 16:10 – 16:40

 

                                    

 

Description:

Physicists Summary

Hugh Lippincott (Fermilab)

Primary author: Jan Conrad

 

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