S681 | 27063 | Steen Andersson

This course requires knowledge in basic statistical theory, in particular the basics in multivariate statistical analysis. The actual content of this course depends on the audience and the teaching method depends on the number of students in the class. The course material will be published on the web. Thus, no textbook is required. The topics will, as a rule, be taught in depth and detail. The content will be chosen from the following list of topics/subtopics: (I) Advanced multivariate models: (i) Conditional models, (ii) Covariance analysis, (iii) Growth curve models, (iv) Symmetry normal models, (v) Graphical normal/discrete models, (iv) Missing data normal models, (v) Mixture models. (II) Eigenvalues: (i) Canonical correlations, (ii) Principal component analysis, (iii) Factor analysis models, (iv) Discriminant/classification analysis, (v) Testing and eigenvalues in multivariate normal models. (III) Exponential families: (i) Generalized linear models, (ii) Multivariate logistic regression, (iii) Rasch measurement models, (iv) Conjugated priors, (v) EM/scorings algorithm, (vi) Testing in exponential families, (vii) Models in the Wishart distributions. References: A.J. Izenman (2008) Modern Multivariate Statistical Techniques, Springer. Brian S. Everitt (2005) An R and S-Plus Companion to Multivariate Analysis, Springer.