Oliver Kirchkamp


(here is information for the course on Bayesian Methods)
  • 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 and R has the advantage of being free and rather powerful.

For the Bayesian parts we will use JAGS. It helps if you have installed R and JAGS on your computer when we start the course.

  • Documentation for R is provided via the built in help system but also through the R Homepage. Useful are
    • 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.)
    • On the JAGS Homepage you go to the files pages, then to Manuals, to find the JAGS user manual.
  • 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:
    Start Rgui.exe and install packages from the menu Packages / Install Packages).
    Installing packages from advanced operating systems:
    From within R use the command install.packages("Ecdat"), e.g., to install the package Ecdat
  • In the lecture we will use RStudio as a front end.