Advanced Statistical Methods
Being able to apply the selected advanced statistical methods in practical situations, and being able to interpret the results. Being able to use the R language for running the selected statistical methods.
This course builds on the Statistical Methods course. It extends to more advanced statistical multivariate analysis techniques and their application in business and economics. A selection of the following techniques will be treated: confirmatory factor analysis, structural equations models (Lisrel), generalized linear models, multilevel methods, social network analysis, support vector machines, correspondence analysis, and unfolding. Much attention is given to the application in practical situations and the interpretation of the techniques in empirical research in economics and business. Students apply the techniques using the statistical language R. It is assumed that the students have followed the Statistical Methods course.
• Kuhnert, P. and Venables, B. (2005). An Introduction to R: Software for Statistical Modelling & Computing. CSIRO Mathematical and Information Sciences. Cleveland, Australia.
• Selected chapters of Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning. Springer-Verlag, New York, ISBN 978-1-4614-7137-0, doi: 10.1007/978-1-4614-7138-7,978-1-4614-7138-7, (freely downloadable at www-bcf.usc.edu/~gareth/ISL/)
• Selected papers.
Prerequisite for this course is BERMMC004 Statistical Methods. It is advised that students bring their laptop and have the R language installed. R can be downloaded for free via http://www.r-project.org/ (download from CRAN). It is also recommended to install R-Studio from http://rstudio.org/.
More information and detailed timetables can be found here.
ERIM PhD candidates and Research Master students can register for this course via SIN Online.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to email@example.com by four weeks prior to the start of the course. Please note that the number of places for this course is limited. For external participants, the course fee is 260 euro per ECTS credit.