Lecturer: Markus Pauly and Sarah Friedrich

Exercises: Burim Ramosaj and Thilo Welz

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General Information

Language	English
Lectures	4 h
Exercises	2 h

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Time and Venue

Lectures	Monday, 12 - 2 p.m., He 18, 120 Thursday, 8 - 10 a.m., He 18, 120
Exercise	Tuesday, 4 - 6 p.m., He 22, E.04

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Exam: 

Final Exam: in Heho 18, Room 120 on Monday, July 16, 2018 from 11.30am - 02.00pm

Retake Exam: tba.

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General Informations:

Prerequisites:	Analysis I-II; Linear Algebra I-II; Stochastics I; Elementary Probability and Statistics
Exam:	In order to be admitted to the exam, students must have achieved at least 40% of all exercise points.

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Aims:

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.

Contents:

• 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

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Exercise Sheets:

The exercise sheets are on Moodle.

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Literature: click here

Semesterapparat:  click here