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| STAT0201-3 | Multivariate statistics
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| Duration : | 30h Th, 10h Pr, 20h Mon. WS |
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| Number of credits : |
| Master in Mathematical Sciences, in-depth approach, 1st year | | 8 |
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| Master in Mathematical Sciences, didactic approach, 1st year | | 8 |
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| Master in Mathematical Sciences, professional focus in management, 1st year | | 8 |
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| Master in Mathematical Sciences, professional focus in computer science, 1st year | | 8 |
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| Master en sciences mathématiques, à finalité spécialisée en statistique, 1st year | | 8 |
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| Master en sciences mathématiques, à finalité spécialisée en statistique, 2nd year | | 8 |
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| Master in Mathematical Sciences, specialized approach, 1st year | | 8 |
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| Master in Mathematical Sciences | | 8 |
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| Lecturer : | Gentiane Haesbroeck |
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Language(s) of instruction :
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| French language |
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Organisation and examination :
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| Teaching in the first semester, examination in June |
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Course contents :
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| The theoretical course is subdivided as follows:
Part I:
- Random vectors, multivariate distributions, mean vector, disperson matrix and correlation matrix
- The multinormal distribution and its properties
- Hotelling T² test for comparing two mean vectors
Part II:
- Principal component analysis
- Clustering
- Discriminant analysis
Part III (depending on the available time):
- Introduction to Robust Statistics
- Some recent developpments (depth measures, regularised estimation,...) |
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Learning outcomes of the course :
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| 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,... |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| Probability and inferential statistics courses are required for this course. |
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Planned learning activities and teaching methods :
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| Practicals include:
- solving theoretical problems in multivariate statistics
- using the statistical package R |
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Mode of delivery (face-to-face ; distance-learning) :
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| The course is officially scheduled in the first quarter of the academic year. Depending on the number of enrolled students for that course, lectures will be taught face-to-face (at least 3 students are required) or reading material will be distributed and discussed on a regular basis. |
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Recommended or required readings :
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| 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. |
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Assessment methods and criteria :
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| Students will have to complete a personal project. An oral exam will be organized for the theory while some exercises will be presented in a written exam. |
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Work placement(s) :
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Organizational remarks :
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Contacts :
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| Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), g.haesbroeck@ulg.ac.be |
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