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Course Description
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Course Name
Multivariate Statistics
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Host University
Vrije Universiteit Amsterdam
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Location
Amsterdam, The Netherlands
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Area of Study
Statistics
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Language Level
Taught In English
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Prerequisites
Linear Algebra, Statistics, Introduction to Data Science, Calculus, Numerical Analysis, Econometrics 1
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Course Level Recommendations
Upper
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
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ECTS Credits
6 -
Recommended U.S. Semester Credits3
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Recommended U.S. Quarter Units4
Hours & Credits
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Overview
COURSE OBJECTIVE
Upon completing this course, students have a thorough knowledge of multivariate distributional theory and its relevance for econometrics
and data science, as well as of core dimension reduction and classification methods for multivariate data. Upon completion, students
can understand and apply core analytical derivations and also operationalize their techniques on real or simulated data using code/scripts in Python or R.COURSE CONTENT
This course introduces the theory and applications for analyzing multi-dimensional data. Topics include multivariate distributions,
transformation of variables, Gaussian models, fat-tailed multivariate distributions, copulas, mixture models, multivariate inference and the
Delta method, dimension reduction methods such as principal components and factor models, and clustering methods.TEACHING METHODS
There are two 2h lecture sessions and one 2h tutorial per weekTYPE OF ASSESSMENT
Written exam plus assignments.
Course Disclaimer
Courses and course hours of instruction are subject to change.
Some courses may require additional fees.