3 credits
15.0 h
Q2
Teacher(s)
Lederer Johannes (compensates Van Keilegom Ingrid); Van Keilegom Ingrid;
Language
English
Prerequisites
LSTAT2040 Analyse statistique I
Main themes
The course focuses on empirical processes and on techniques to do inference for semiparametric models in statistics.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
By the end of this class, the student will be able to understand the basic concepts of empirical processes and will be able to apply these concepts to do inference in semiparametric |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The course outline is as follows:
1. Introduction
1. Introduction
- 'Semiparametric models
- 'Semiparametric Z-estimators
- 'Review of the basics of stochastic processes
- 'Introduction to modern empirical process theory
- 'Examples
Other information
The course material consists of a syllabus. A pdf file of the syllabus will be made available to the students.
Bibliography
- 'Billingsley, P. (1968). Convergence of Probability Measures , Wiley, New York.
- 'Newey, W.K. (1994). The asymptotic variance of semiparametric estimators. Econometrica, 62, 1349'1382.
- 'Van der Vaart, A. and Wellner, J.A. (1996). Weak Convergence and Empirical Processes. Springer, New York.
Faculty or entity
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Statistics: General
