Oliver Kirchkamp
[A picture of Oliver Kirchkamp]


Date: Monday, 29.7.2019 - Friday 2.8.2019
  • Resampling methods, bootstraps, jacknife, ...
    1. Introduction
    2. Parameters, distribution and the plug-in principle
    3. Estimating standard errors
    4. More complicated data structures
    5. Bias
    6. Confidence intervals
    7. Hypothesis testing
    To prepare or to revise, please have a look at the handout:
    Download Bootstrap Handout
Exercises can be found in the handout (datasets are attached to the PDF).
  • Julian J. Faraway, Extending the Linear Model with R. Chapman & Hall, 2006.
  • A. C. Davison, D. V. Hinkley, Bootstrap Methods and their Application, Cambridge University Press, 1997
  • Bradley Efron and Robert J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, 1994
For our practical examples (during the entire course) we will use the software environment R. I think that it is helpful to coordinate on one environment. R is free, it is very powerful, and it is popular in the field.
  • Documentation for R is provided throught the built in help. You also find support on the R Homepage. You might find the following useful:
    • The R Guide, Jason Owen (Easy to read, explains R with the help of examples from basic statistics)
    • Simple R, John Verzani (Explains R with the help of examples from basic statistics)
    • Einführung in R, Günther Sawitzki (In German. Rather compact introduction.)
    • Econometrics in R, Grant V. Farnsworth (The introduction to R is rather compact and pragmatic.)
    • An Introduction to R, W. N. Venables und D. M. Smith (The focus is more on R as a programming language)
    • The R language definition (Concentrates only on R as a programming language.)
  • You can download R from the homepage of the R-project.
    Installing R with Microsoft Windows:
    Download and start the Installer. Install R on your local drive. Installing on a network drive or in the cloud (Dropbox, Onedrive,...) is possible but not recommended.
    Installing R with GNU-Linux:
    Follow the advice to install R for your distribution.
    Installing R with MacOS X:
    Here is a guide to install R with MacOS X.
  • In the lecture we use RStudio as a front end.
  • We will use the following packages: boot, bootstrap, car, Ecdat, Hmisc, lattice, latticeExtra, lme4, mvtnorm.

    If, e.g., the command library(Ecdat) generates an error message (Error in library(Ecdat): There is no package called 'Ecdat'), you have to install the package.

    Installing packages with Microsoft Windows:
    With RStudio: Use the tab “Install”. Otherwise: Start Rgui.exe and install packages from the menu Packages / Install Packages).
    Installing packages from GNU-Linux or MacOS X:
    From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat