Program

PROGRAM

The program for the workshop and Winter School is now in its final form. Given the large student attendance, we decided to start each session with a short lecture to introduce the topic, followed by presentations by senior scientists and by other participants. (P) is for a presentation in-person, (V) for virtual/remote presentation. All participants who requested a presentation have been allocated a talk. (See full program below)



DATA CHALLENGES

Given high participation from students, we have a few data challenges associated with presentations and methods discussed in this workshop


Data Challenge 1 (see "Overview of regression methods for count data" in Session 1)

Perform a linear regression of the following dataset, representing the number of daily COVID-19 events in the first ten days of the pandemic (as reported by the New York Times; the first number corresponds to the day prior to the first reported case ):

0 1 2 3 4 2 0 3 4 3

Specifically, determine the slope (with uncertainty) of the linear regression of these counts versus time (in days)



Data Challenge 2: Flux estimation from event data (see Talk by D. Mortlock)

Instructions and Challenge: count_flux.pdf

Associated Datasets:

  1. Data Set #1

  2. Data Set #2

  3. Data Set #3


ProgramFinal

List of Title and Abstracts of Presentations

Complete Titles and Abstracts.pdf

Main Topics

  • General methods of statistical estimation: Maximum-likelihood (ML), likelihood ratio, random processes and associated distributions for event data.

  • Statistics for spatial analysis in astronomy, including examination of clustering and other spatial structures

  • Statistics for spectral data, including Poisson--based likelihood--ratio methods such as the Cash statistic.

  • Methods for various source sizes, or by type (e.g., i diffuse, structured, compact sources, etc.), or messenger type (e.g., ground--based observations, space--based, in-situ, particles)

  • Statistics to identify sources in low count-rate data: Poisson probabilities, Li-Ma and Feldman-Cousins criteria, Bayesian approaches.

  • Statistics for temporal analysis of low count-rate data: tests for variability, Bayesian Block models, autoregressive models

  • Linear and non linear methods of regression: weighted least--squares, ML, parameter estimation and goodness-of-fit, error estimation and characterization

  • Specialized methods in the low count-rate regime and biases from the use of Gaussian approximations for event data

  • Point processes, Markov chains and other stochastic processes

  • Binomial and multinomial models, contingency tables and logistic regression

  • Upper and lower limits, censored and truncated data

  • Statistics for numerical methods (e.g., Monte Carlo, MCMC, machine learning, resampling methods like jackknife and bootstrap)

  • Software development in support of statistical data analysis for astronomy and beyond