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:
List of Title and Abstracts of Presentations
![](https://www.google.com/images/icons/product/drive-32.png)
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