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This entry is from Winter semester 2019/20 and might be obsolete. No current equivalent could be found.
High-dimensional Statistics
(dt. Hochdimensionale Statistik)
Level, degree of commitment | Specialization module, compulsory elective module |
Forms of teaching and learning, workload |
Lecture (3 SWS), recitation class (1 SWS), 180 hours (60 h attendance, 120 h private study) |
Credit points, formal requirements |
6 CP Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises. Examination type: Written or oral examination |
Language, Grading |
German,The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Business Mathematics. |
Duration, frequency |
One semester, Regularly alternating with other specialization modules |
Person in charge of the module's outline | Prof. Dr. Hajo Holzmann |
Contents
- Linear regression model and least squares
- Penalization with lasso, BIC,
- sparsity
- Convergence rates with respect to the mean squared prediction error and the mean squared error
- lower bounds
- variable selection
- elements of estimating large matrices, such as covariance matrices, principal component analysis
Qualification Goals
The students shall
- acquire theoretical knowledge of current research in the field of high-dimensional statistics,
- get to know important algorithms and their functionality in the programming language R,
- practice mathematical working methods (development of mathematical intuition and its formal justification, training of abstraction, proof methods),
- improve their oral communication skills in the recitation class by practicing free speech in front of an audience and during discussion.
Prerequisites
None. The competences taught in the following modules are recommended: either Foundations of Mathematics and Linear Algebra I and Linear Algebra II or Basic Linear Algebra, either Analysis I and Analysis II or Basic Real Analysis, Mathematical Statistics.
Applicability
Module imported from M.Sc. Business Mathematics.
It can be attended at FB12 in study program(s)
- B.Sc. Mathematics
- B.Sc. Business Mathematics
- M.Sc. Data Science
- M.Sc. Mathematics
- M.Sc. Business Mathematics
When studying B.Sc. Mathematics, this module can be attended in the study area Compulsory Elective Modules in Mathematics.
Die Wahlmöglichkeit des Moduls ist dadurch beschränkt, dass es der Angewandten Mathematics zugeordnet ist.
Recommended Reading
- Bühlmann, P., van de Geer, S. „Statistics for High-Dimensional Data“, Springer 2011
Please note:
This page describes a module according to the latest valid module guide in Winter semester 2019/20. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:
- Winter 2016/17 (no corresponding element)
- Summer 2018
- Winter 2018/19
- Winter 2019/20
- Winter 2020/21
- Summer 2021
- Winter 2021/22
- Winter 2022/23
- Winter 2023/24 (no corresponding element)
The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.
The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.