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Institute of Stochastics

Secretariat
Kollegiengebäude Mathematik (20.30)
Room 2.056 und 2.002

Address
Hausadresse:
Karlsruher Institut für Technologie (KIT)
Institut für Stochastik
Englerstr. 2
D-76131 Karlsruhe

Postadresse:
Karlsruher Institut für Technologie (KIT)
Institut für Stochastik
Postfach 6980
D-76049 Karlsruhe

Office hours:
Mo-Fr 10:00 - 12:00

Tel.: +49 721 608 43270/43265

Fax.: +49 721 608 46066

Asymptotic Stochastics (Winter Semester 2015/16)

Lecturer: Prof. Dr. Vicky Fasen-Hartmann
Classes: Lecture (0118000), Problem class (0118100)
Weekly hours: 4+2


Aktuelles


Die Vorlesung am Donnerstag, 17. 12. 2015, muss wegen Krankheit leider kurzfristig entfallen.

Schedule
Lecture: Wednesday 8:00-9:30 SR 0.014
Thursday 11:30-13:00 SR 0.014
Problem class: Friday 9:45-11:15 SR 0.014
Lecturers
Lecturer Prof. Dr. Vicky Fasen-Hartmann
Office hours: On appointment.
Room 2.053 Kollegiengebäude Mathematik (20.30)
Email: vicky.fasen@kit.edu
Problem classes Dr. Sebastian Kimmig
Office hours: Mittwoch 14:00 - 15:00 Uhr und nach Vereinbarung
Room 2.011 Kollegiengebäude Mathematik (20.30)
Email: sebastian.kimmig@kit.edu

Content

  • Basic facts from probability theory
  • Convergence in R^d (the multivariate normal distribution, Convergence in distribution,CLT in R^d)
  • Empirical distribution functions
  • A CLT for m-dependent stationary sequences
  • Estimation theory (Asymptotic properties of maximum likelihood estimators,Asymptotic (relative) efficiency of estimators, Likelihood ratio test)
  • U-statistics
  • Probability measures on metric spaces (weak convergence in metric spaces, relative compactness and tightness, weak convergence and tightness in C)
  • Donsker's theorem
  • Empirical processes: applications in statistics.

Prerequisites

A sound working knowledge in measure-theory based on probability theory
(especially strong law of large numbers, convergence in distribution in R^1, Central limit
theorem of Lindeberg-Lévy), and statistical concepts (tests, confidence regions).


Material

Course Material and important information can be found in the ILIAS course of the lecture.



Examination

There will be oral examinations towards the end of the semester. All further details can be found in the ILIAS course of the lecture.

References

  • Billingsley, P. (1986): Probability and Measure. Wiley, New York.
  • Billingsley, P. (1968): Convergence of probability measures. Wiley, New York.
  • Ferguson, Th.S. (1996): A Course in Large Sample Theory. Chapman & Hall, London.
  • Lee, A.J. (1990): U-Statistics. Theory and practice. Marcel Dekker, New York, Basel.
  • Lehmann, E.L. (1999): Elements of large sample theory. Springer, New York.
  • Shao, J. (2003): Mathematical Statistics. Second edition. Springer, New York.