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The aim of this course is to present the statistical approaches for analysing multivariate data. The information age has resulted in masses of multivariate data in many different field: finance, marketing, economy, biology, environmental sciences,...The theoretical and practical aspects of multivariate data analysis are given equal importance. This balance is achieved through practicals involving actual data analysis using the R software.
Contact Jean-Baptiste DURAND1. Multiple linear regression. Least squares, Gaussian linear model, test of linear hypotheses
2 One-way and two-way analysis of variance.
3. Principal Components Analysis (PCA).
4. Classification, supervised classification, linear discriminant analysis, unsupervised classification, K-means.
5. Document and pattern mining on graphs.
Applied Probability 2 (1st year), Statistical Principles and Methods (Semester 2)
Practical exam with R (2 h) and 3 reports on supervised practicals.
N1=1/2E1+1/2P
N2=E2
CM BISHOP (2006) Pattern recognition and machine Learning. Springer
http://research.microsoft.com/en-us/um/people/cmbishop/prml/
C. CHATFIELD and AJ COLLINS (1980) Introduction to multivariate analysis. Science paperbacks
T HASTIE, R TIBSHIRANI, and J FRIEDMAN (2009). The Elements of Statistical Learning, 2d ed, Springer. http://www-stat.stanford.edu/~tibs/ElemStatLearn/
G. SAPORTA : Probabilités, statistique et analyse des données, Technip, 2006.
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