Lecturer: Markus Pauly and Sarah Friedrich
Exercises: Burim Ramosaj and Thilo Welz
Time and Venue
Monday, 12 - 2 p.m., He 18, 120
Thursday, 8 - 10 a.m., He 18, 120
Tuesday, 4 - 6 p.m., He 22, E.04
Final Exam: in Heho 18, Room 120 on Monday, July 16, 2018 from 11.30am - 02.00pm
Retake Exam: tba.
|Prerequisites:||Analysis I-II; Linear Algebra I-II; Stochastics I; Elementary Probability and Statistics|
In order to be admitted to the exam, students must have achieved at least 40% of all exercise points.
Multivariate analysis is in principle a collection of methods designed to elicit information from multivariate data and to answer different statistical questions of interest.
In particular, students will
- get to know different (parametric and nonparametric) statistical models which are most popular for describing multivariate data in practice and will
- be familiar with the corresponding inference procedures as hypothesis tests (e.g. Wilk's Lambda) and confidence ellipsoids,
- learn about specific classification and grouping methods and their properties and
- be able to apply their knowledge to real data.
- Finally, if there is enough time left, we will also treat modern statistical learning techniques for (multivariate) classification and prediction problems.
- Data visualization. How to present multivariate data?
- Hypothesis construction and testing; e.g. likelihood-ratio-tests and nonparametric tests
- Confidence ellipsoids
- Dimension reduction or structural simplification and their limitations
- Investigation of dependence among variables
- Bootstrap for multivariate data
- Classification and Prediction
The exercise sheets are on Moodle.
Literature: click here
Semesterapparat: click here
Final Exam on Monday, July 16, 2018 in Heho 18, Room 120 starting at 11.30am !!!