Thèmes choisis de statistique multivariée
UE-EIG.00029
Teacher(s): Donzé Laurent |
Level: Master |
Type of lesson: Lecture |
ECTS: 4.5 |
Language(s): French |
Semester(s): AS-2023 |
The course will focus on some traditional topics in Multivariate Statistics:
- Specific regression methods (Linear models; shrinkage methods; principal components regression and partial least squares regression)
- Principal components analysis (Decomposition of data matrices by factors; principal components analysis; principal factors and principal components)
- Factor analysis (Introduction; orthogonal factor model; total variance and model fit; determination of the number of factors; rotation of factors; estimation of factor scores)
- Structural equation models (Introduction; models PLS-SEM; models CB-SEM)
The theoretical concepts and the methods are illustrated by practical applications. R is the software used.
Moodle
Training aims
The course is part of the supplied of Master courses in applied statistics, which is a packet of four half-yearly ones of 4.5 ECTS (one by semester) over two years:
- Topics in multivariate statistics
- Introduction to Bayesian statistics
- Inference, evaluation, and selection of models
- Classification methods
Although they complete each other, they can be chosen separately. Of general interest, the set of courses gives a broad view of problems and applied statistical methods, and of the data science. A server of Jupyter notebooks completes the course.
There is no specific public. Although the courses are primarily conceived for students of the Faculty of Management, Economics and Social Sciences, they can be attended by other students.
The student will benefit not only of theoretical knowledge but is also skilled in the use of the methods presented during the course.
Documentation
A script with a listing of references is provided. The student will find on the course's Moodle platform other resources.