Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
3 credits
15.0 h
Q2
Teacher(s)
Pircalabelu Eugen;
Language
English
Prerequisites
LSTAT2120 Linear models & LSTAT2040 Analyse statistique I
Main themes
The course focuses on high-dimensional settings and on techniques to that
allow parameter estimation for high-dimensional models in statistics.
allow parameter estimation for high-dimensional models in statistics.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
A. Eu égard au référentiel AA du programme de master en statistique, orientation générale, cette activité contribue au développement et à l'acquisition des AA suivants, de manière prioritaire : 1.4, 1.5, 2.4, 4.3, 6.1, 6.2. B. By the end of this class, the student will be able to understand the basic concepts of penalized estimation and will be able to apply these concepts to perform estimation/inference for high-dimensional 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”.
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
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.
Teaching materials
- Syllabus disponible sur moodle.
Faculty or entity
LSBA
