Bootstrap
(here is information for the course on Bayesian Methods)- 19. 06. 2017, 10:15-11:45: Nora-Platiel 9 - Raum 0402
- 20. 06. 2017, 10:15-11:45: Nora-Platiel 9 - Raum 0402
- 26. 06. 2017, 10:15-11:45: Kurt-Schumacher 25, R. 2210/2212
- 27. 06. 2017, 10:15-11:45: Kurt-Schumacher 25, R. 2210/2212
- 28. 06. 2017, 10:15-11:45: Kurt-Schumacher 25, R. 2210/2212
- 06. 07. 2017, 13:00-14:00: Exam
- Topics
-
- Resampling methods, bootstraps, jacknife, ...
- Introduction
- Parameters, distribution and the plug-in principle
- Estimating standard errors
- More complicated data structures
- Bias
- Confidence intervals
- Hypothesis testing
- Resampling methods, bootstraps, jacknife, ...
- Exercises
- Exercises can be found in the handout (datasets are attached to the PDF).
- Literature:
-
- 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
- Software
-
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 menuPackages / Install Packages
). - Installing packages from GNU-Linux or MacOS X:
- From within R use the command
install.packages("Ecdat")
, e.g., to install the packageEcdat
- Documentation for R is provided throught the built in help. You also find support on the R Homepage.
You might find the following useful: