MULTIVARIATE ANALYSIS

Course Information
TitleΠΟΛΥΜΕΤΑΒΛΗΤΗ ΑΝΑΛΥΣΗ / MULTIVARIATE ANALYSIS
CodeΣΜΥ018
FacultySciences
SchoolMathematics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter/Spring
CoordinatorGeorgios Afendras
CommonNo
StatusActive
Course ID600025954

Programme of Study: PMS Tmīmatos Mathīmatikṓn (2025-2030)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
STATISTIKĪ, MONTELOPOIĪSĪ KAI YPOLOGISTIKES METHODOIElective Courses belonging to the selected specializationSpring-10
Class Information
Academic Year2025 – 2026
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Total Hours39
Class ID
600268436
Course Type 2021
General Foundation
Course Type 2016-2020
  • Scientific Area
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
General Background on Linear Algebra, Probability Theory and Statistics at the level of undergraduate course
Learning Outcomes
Upon successful completion of the course, students will: 1. have acquired the basic knowledge of multivariate random variables, 2. have acquired knowledge of the common used multivariate distributions (multivariate normal distribution, polynomial distribution, negative polynomial distribution, multivariate t distribution, Dirichelt distribution, family of elliptic distributions), 3. be able to understand and manage the theory and tools of the order statistics, 4. be able to draw statistical results (estimate and statistical inference) for the parameters of the multivariate normal distribution, 5. they will be able to use the statistical tools of principal components and discriminant analysis.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Random Vectors, Common Used Multivariate Distributions, Parametric Estimation of the Multivariate Normal Distribution, Hypothesis Testing for the Parameters of the Multivariate Normal Distribution, Principal Component Analysis, Discriminant Analysis.
Keywords
Random Vectors, Parametric Estimation, Hypothesis Testing, Principal Component Analysis, Discriminant Analysis.
Educational Material Types
  • Notes
  • Interactive excersises
  • Book
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39✓
Fieldwork108✓
Reading Assigment150✓
Exams3✓
Total300
Student Assessment
Description
Homework/Presentation 60% Final Examination 40%
Student Assessment methods
  • Written Assignment (Formative, Summative)
  • Oral Exams (Formative, Summative)
Bibliography
Additional bibliography for study
Anderson, T.W. (1984). An Introduction to Multivariate Statistical Methods. 2nd ed., Wiley. DasGupta, A. (2008). Asymptotic theory of statistics and probability. Springer Science & Business Media. Giri, N.J. (1996). Multivariate Statistical Analysis. Marcel Dekker, New York. Hogg, R.V. and Graig, A.T. (1970). Intoduction to Mathematical Statistics, 3rd ed. The Macmillan Company, London. Johnson, R.A. and Wichern, D.W. (1992). Applied Multivariate Statistical Analysis. Prentice Hall. Kendall, M.G. (1948). The advanced theory of statistics. Vols. 1. The advanced theory of statistics. Vols. 1(Ed. 4). Lehmann, E.L. and Casella, G. (2006). Theory of point estimation. Springer Science & Business Media. Muirhead, R.J. (1982). Aspects of Multivariate Statistical Theory. Wiley, New York. Serfling, R.J. (2009). Approximation theorems of mathematical statistics (Vol. 162). John Wiley & Sons. Shao, J. (2007). Mathematical Statistics 2nd edition. Springer Science & Business Media.
Last Update
16-05-2025