Lecturer(s)


Müller Ivo, RNDr. PhDr. Ph.D.

Hron Karel, doc. RNDr. Ph.D.

Course content

1 Introduction, classification of methods, elementary notions and problems. 2 Multivariate normal distribution, basic properties. 3 Characterization theorems, conditional normal distribution. 4 Normal regression, partial and multiple correlations. 5 Parameter estimation: unbiased, maximum likelihood. 6 Wishart?s distribution, properties. 7 Transformations of Wishart?s distribution, Hotelling?s statistic. 8 Tests of hypotheses, confidence regions, simultaneous tests. 9 Principal components. 10 Canonical correlations. 11 Discrimination analysis. 12 Factor analysis. Cluster analysis.

Learning activities and teaching methods

Lecture
 Attendace
 52 hours per semester
 Preparation for the Course Credit
 22 hours per semester
 Preparation for the Exam
 50 hours per semester

Learning outcomes

Mastering the principles of multivariate statistical estimation and testing based on normal distributional theory.
Comprehension Mastering the principles of multivariate statistical estimation and testing based on normal distributional theory.

Prerequisites

Motivation to learn

Assessment methods and criteria

Oral exam, Written exam
Credit: active participation in seminars, written test. Exam: oral.

Recommended literature


R.C. Rao. (1978). Lineární metody statistické indukce a jejich aplikace. Academia.

T.W. Anderson. (1984). An Introduction to Multivariate Statistical Analysis. Wiley.
