Multivariate statistics (2017)
Your midterm (and preexam) points are below. I can write your offered grade to the NEPTUN only if you take an exam via NEPTUN. You can as well come to the oral exam if want to improve your grade.

Your midterm results and the offered grades

Topics of the final exam

Course requirements

Lesson 1 (Linear algebra and random vectors, basics for the whole semester)

Lesson 2 (Multivariate normal distribution)

Lesson 3 (Parameter estimation in multivariate models)

Addendum to Lesson 3: CramerRao inequality for multidimensional parameter functions

Lesson 4 (ML estimation in multivariate normal models and
the distribution of the estimators)

Addendum to Lesson 4 (Multivariate normal distribution as an exponential family distribution)

Supplementary material (slides to parameter space, ML estimation, Lukacs theorem, and derivation of the Wishart density)

Lesson 5 (Hypothesis testing on the multivariate normal mean vector)

Lesson 6 (Multivariate regression)

Addendum to Lesson 6 (Partitioned covariance matrices, partial correlations and Gaussian graphical models)

Lesson 7 (Generalized linear models, ANOVA, time series,
econometrics, examples)

Lesson 8 (Principal Component and Factor Analysis)

Lesson 9 (Canonical Correlation Analysis)

Lesson 10 (Discriminant Analysis)

Lesson 11 (Dynamic Factor Analysis)

Lesson 12 (Clustering and Multidimensional Scaling)

Lesson 13 (Correspondence Analysis)

Table of notable distributions

Percentile values of the standard normal, t, and chisquare distributions

Percentile values of the Fdistribution

Formulas of statistical tests and regression

ANOVA tables

BMDP outputs

Homework I. (due on 16th October)

Homework II. (due on 27th November)

Államvizsga tematika (MSC képzés)

Closing (state) exam questions (MSC)