LSTAT2040 Analyse statistique I
The course focuses on empirical processes and on techniques to do inference for semiparametric models in statistics.
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
models in statistics.
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”.
The course outline is as follows:
1. Introduction
- 'Semiparametric models
- 'Semiparametric Z-estimators
2. Empirical processes
- 'Review of the basics of stochastic processes
- 'Introduction to modern empirical process theory
- 'Examples
3. Asymptotics for semiparametric Z-estimators
- '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.
The course material consists of a syllabus. A pdf file of the syllabus will be made available to the students.
