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 |
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:
- Challenges concerning high-dimensional models
- Regularized methods in high-dimensional statistics
- Parameter estimation
- Tuning parameter selection
- Feature selection
- Graphical modeling
- High-dimensional inference
Evaluation methods
The evaluation of the students is project-based.
Other information
The course material consists of slides made available to the students.
Bibliography
- Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical
Learning: Data Mining, Inference, and Prediction. Springer. - Bühlmann, P., van de Geer, S. (2011). Statistics for High-Dimensional Data.
Springer. - Hastie, T., Tibshirani, R. and Wainwright, M. (2015 ).Statistical Learning
with Sparsity: The Lasso and Generalizations. Chapman and
Hall/CRC.
Faculty or entity
LSBA
