Duration
30h Th, 10h Pr, 20h Mon. WS
Number of credits
| Master in mathematics (120 ECTS) | 8 crédits | |||
| Master in mathematics (60 ECTS) | 8 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The theoretical course is subdivided as follows:
Part I: inferential statistics
- Estimation of mean vector and covariance matrix (included for contaminated or psarse models)
- The multivariate normal distribution and test of multinormality
- Hotelling T² test for comparing two mean vectors
Part II: exploratory techniques
- Principal component analysis
- Clustering
- Discriminant analysis
Part III: multivariate ranks and quantiles
- Depth functions and contours
- Multivariate ranks and quantiles
Part IV: independence and copulas
Learning outcomes of the learning unit
The student will gain sufficient knowledge to be able to select the appropriate multivariate technique to reduce the dimension of the problem or construct classification rules,...
Prerequisite knowledge and skills
Probability and inferential statistics courses are required for this course.
Planned learning activities and teaching methods
Practicals include: - solving theoretical problems in multivariate statistics - data analysis with the statistical package R
Mode of delivery (face-to-face ; distance-learning)
The course is officially scheduled in the first quarter of the academic year, on uneven years (il will not be taught in 2018-2019).
Depending on the number of enrolled students for that course, lectures are taught face-to-face (at least 3 students are required) or reading material will be distributed and discussed on a regular basis.
Recommended or required readings
There are no lecture notes. Textbooks are:
- Multivariate statistical inference and applications, Alvin C. RENCHER.
- Applied multivariate statistical analysis, Richard A. Johnson, Dean W. Wichern.
Assessment methods and criteria
The final grade is a weighted mean computed on the grades obtained at two exams taking place in January:
1) a written exam on theory and exercises
2) a data analysis to be performed in the computed room on the same day as the written exam.
Work placement(s)
Organizational remarks
This course is only taught every other year (on uneven years: 2017-2018, 2019-2020).
Contacts
Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), g.haesbroeck@ulg.ac.be