Multivariate statistics

Duration

30h Th, 10h Pr, 20h Mon. WS

Number of credits

Lecturer

Gentiane Haesbroeck

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January

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 - Random vectors, multivariate distributions, mean vector, disperson matrix and correlation matrix - The multivariate normal distribution and its properties - 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

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. 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.

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