Fortgeschrittene Methoden der Biometrie A

Dozent: Michael Vogt

Übungsleiter: Ly Hoang

General Information

Language

Deutsch/Englisch
Lectures 4 h
Excercises 2 h

Lectures s. Moodle

 
Excercises s. Moodle  
Exam s. Moodle  

General Information:

Target Audience: 

  • Master Mathematische Biometrie
  • Master Wirtschaftsmathematik
  • Master Finance
  • Master Mathematik
Course Prerequisites: 

Elementary Probability Calculus and Statistics
 

 

Exam:

Exam dates and further information will be posted on Moodle.


Contents

  • The lecture gives an introduction to asymptotic statistics. In mathematical statistics, many theoretical results are asymptotic in nature. Hence, convergence concepts such as convergence in probability, almost sure convergence and convergence in distribution play a very important role.
  • The first part of the lecture introduces basic concepts of stochastic convergence. It sheds some light on the connection between them and uses them to derive laws of large numbers as well as central limit theorems.
  • The second part of the lecture uses the introduced convergence concepts to analyze the convergence behaviour of certain classes of estimators. Examples are the class of M-estimators (which includes many basic estimators such as least squares, maximum likelihood, GMM, etc.) and the class of nonparametric kernel estimators (which can e.g. be used to estimate densities and regression functions).

Excercise Weekly exercise sheets will be posted on Moodle. The solutions will be discussed weekly in live sessions via zoom every Tuesday from 16:00 - 18:00


 

Literature: 

  • van der Vaart, Asymptotic Statistics, Cambridge University Press

Link zum Semesterapparat

 

Notes

This course will be held completely online (except for exams). Information and material will be provided on Moodle.

Lecturer

Prof. Dr. Michael Vogt

Professor
Institut of Statistics
Helmholtzstraße 20
89081 Ulm
Baden-Württemberg
Germany
Raum: HeHo 20 E.43

Excercise