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Course, academic year 2023/2024
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Multivariate Analysis - NMST539
Title: Mnohorozměrná analýza
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2023 to 2023
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: English, Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: prof. RNDr. Ivan Mizera, CSc.
doc. RNDr. Matúš Maciak, Ph.D.
doc. RNDr. Zdeněk Hlávka, Ph.D.
Class: M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Pre-requisite : {At least one courses in GLM}
Incompatibility : NMFP535
Is incompatible with: NMFP535
Is interchangeable with: NMFP535, NSTP018
Files Comments Added by
download notes0.pdf Lecture Notes part O prof. RNDr. Ivan Mizera, CSc.
download notes1.pdf Lecture Notes part I prof. RNDr. Ivan Mizera, CSc.
download notes2.pdf Lecture Notes part II prof. RNDr. Ivan Mizera, CSc.
download problems.pdf Homework problems prof. RNDr. Ivan Mizera, CSc.
Annotation -
An introduction to traditional and modern methods of multivariate statistics.
Last update: Omelka Marek, doc. Ing., Ph.D. (01.06.2023)
Aim of the course -

Teach students basic methods of multivariate statistical analysis.

Last update: Mizera Ivan, prof. RNDr., CSc. (15.10.2023)
Course completion requirements -

Requirements for obtaining the credit (zápočet): participation in the exercises (max 3 absences) and continual solving of the assigned problems (acquiring at least 36 credits, where one solved problem typically amount to one credit). The nature of these requirements precludes any possibility of additional attempts to obtain the class credit. Acquired credit is a condition for attending the examination, which will be in the written form, and apart from simple questions similar to those covered in the exercises will contain also questions regarding principles, motivations, algorithms, and applications of the techniques covered in the lectures.

Last update: Mizera Ivan, prof. RNDr., CSc. (15.10.2023)
Literature -

Bouveyron C., Celeux G., Murphy T.B., Raftery A. E.: Model-based Clustering and Classification for Data Science: with Applications in R. Cambridge University Press, 2019.

Härdle, W. K., Hlávka, Z.: Multivariate Statistics: Exercises and Solutions, 2nd edition, Springer, 2015.

Härdle W. K., Simar L.: Applied Multivariate Statistical Analysis, 4th edition, Springer, 2015.

Mardia K.V., Kent J.T., Bibby J.M.: Multivariate Analysis. Academia Press. London, 1979.

Rao C.R.: Linear Statistical Inference and Its Applications. 2nd edition. Wiley. New York, 1973.

Venables W.N. Ripley B.D.: Modern Applied Statistics with S, 4th edition, Springer, 2002.

Last update: Hlávka Zdeněk, doc. RNDr., Ph.D. (08.12.2020)
Teaching methods -

Lecture+exercises.

Last update: T_KPMS (16.05.2013)
Requirements to the exam -

The exam will be in written form.

Last update: Mizera Ivan, prof. RNDr., CSc. (03.10.2023)
Syllabus -

1. Multivariate normal distribution.

2. Wishart and Hotelling distribution.

3. Multivariate statistical inference.

4. Principal components and factor analysis.

5. Canonical correlations, correspondence analysis.

6. Discriminant and cluster analysis.

7. Projections-based methods, data depth.

8. Statistical software.

Last update: Hlávka Zdeněk, doc. RNDr., Ph.D. (08.12.2020)
Entry requirements -

basic knowledge of linear algebra, theory of probability, and mathematical statistics

Last update: Hlávka Zdeněk, doc. RNDr., Ph.D. (25.05.2018)
 
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