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This entry is from Winter semester 2022/23 and might be obsolete. No current equivalent could be found.

Nonparametric Statistics
(dt. Nichtparametrische Statistik)

Level, degree of commitment Specialization module, depends on importing study program
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 or English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Business Mathematics.
Subject, Origin Mathematics, M.Sc. Business Mathematics, M.Sc. Business Mathematics
Duration,
frequency		 One semester,
Regularly alternating with other specialization modules
Person in charge of the module's outline Prof. Dr. Markus Bibinger, Prof. Dr. Hajo Holzmann

Contents

  • Non-parametric density estimation, core estimator, projection estimator, upper barriers for point by point, uniform and integrated risk
  • Lower bounds and optimal rates
  • Lepski's scheme and adaptivity
  • Nonparametric regression, local polynomial estimators
  • Model in white noise, Pinsker estimation and Pinsker constant
  • Wavelet estimation


Qualification Goals

The students shall

  • get an insight into a current research area of nonparametric statistics,
  • acquire the basic techniques within the field of nonparametric statistics,
  • practice mathematical methods (development of mathematical intuition and its formal justification, training of the ability to abstract, proof methods),
  • improve their oral communication skills in the recitation classes 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.


Recommended Reading

  • Tsybakov, A. (2009) Introduction to nonparametric estimation. Springer
  • Johnstone, I. (2013) Gaussian estimation: Sequence and wavelet models.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2022/23. 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:

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.