Workshop videos

StatCon 2021 On-line Workshop

19 November, 2021 – 9 December, 2021

 

Videos and Slideshows

Home

 

Leonard Santana  - North-West University, Potchefstroom

WORKSHOP TITLE:  A beginner's guide to the bootstrap :

Chapter 0: An introduction to Monte Carlo methods
    - Introduction to Monte Carlo methods
    - Approximating the expected value of a statistic
    - Approximating the standard error of a statistic
    - Approximating probabilities
    - Approximating quantiles
    - When do we need to use Monte Carlo methods, and why is it being discussed in a course on the bootstrap?
    - R tutorial: Generating data using the inverse transform method
    - R tutorial: Example of Monte Carlso approximated expected values, standard errors, probabilities, and quantiles

Chapter 1: An introduction to the bootstrap
    - An introduction to the bootstrap
    - Introducing notation used throughout
    - The empirical distribution function (EDF) and its use in the bootstrap (Bootstrap.Chapter01.03.E.D.F.(A)-1.m4v, and Bootstrap.Chapter01.04.E.D.F.(B)-1.m4v, and Bootstrap.Chapter01.05.E.D.F.(C).Drawing.Samples-1.m4v)
    - The plug-in principle (Bootstrap.Chapter01.06(A).Plugin.Principle-1.m4v, and Bootstrap.Chapter01.06(B).Plugin.Principle.Examples-1.m4v)
    - The general bootstrap algorithm
    - When is this computer intensive bootstrap algorithm not necessary? An example with the sample mean
    - The double bootstrap: estimating the standard error of the bootstrap estimate of standard error
    - Example of situations where the bootstrap does not work

Chapter 2: Different ways of estimating the distribution function
    - Introduction
    - The nonparametric bootstrap: recap
    - The parametric bootstrap and example (Bootstrap.Chapter02.03.Fpar-3.m4v, and Bootstrap.Chapter02.04.Fpar.Example-4.m4v)
- The semi-parametric bootstrap and examples (Bootstrap.Chapter02.05.Fsemi-5.m4v, and Bootstrap.Chapter02.06.Fsemi.Model.Based.Method.&.Example-6.m4v, and Bootstrap.Chapter02.07.Fsemi.Example.Where.E(X)=10-1.m4v, and Bootstrap.Chapter02.08.Fsemi.Example.Symmetry-1.m4v)
    - The smoothed bootstrap and kernel distribution estimation (Bootstrap.Chapter02.09.Smoothing.&.Kernel.Density-1.m4v, and Bootstrap.Chapter02.10.Fh.Kernel.Distribution.Estimator-1.m4v, and Bootstrap.Chapter02.11.Fh.Drawing.Samples-1.m4v, and Bootstrap.Chapter02.12.Fh.Correction.To.Drawing.Samples-1.m4v, and Bootstrap.Chapter02.13.Fh.Example-1.m4v)

Chapter 6: Regression and the bootstrap
    - Introduction and review of regression concepts
    - The residuals-based (or model-based) approach to bootstrap regression (Bootstrap.Chapter06.02.Bootstrap.Residuals.Approach.A-1.m4v, and Bootstrap.Chapter06.03.Bootstrap.Residuals.Approach.B-1.m4v)
    - The paired or cases approach to bootstrap regression
    - The wild bootstrap

Workshop slides

Bojana Milosevic - University of Belgrade, Serbia

Characterization based goodness of fit tests - construction and properties  
Videos:

  1. Motivation and Characterizations
  2. Theory of U statistics
  3. Test I_n
  4. Tests D_n and W_n
  5. Laplace transform based tests and others
  6. Tests based on independence type characterizations
  7. Tests based on functional equations
  8. Summary

 
Workshop slides