Course Unit Page
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Teacher Silvia Cagnone
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Credits 6
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SSD SECS-S/01
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Language Italian
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Campus of Bologna
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Degree Programme Second cycle degree programme (LM) in Economics (cod. 8408)
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Course Timetable from Sep 21, 2020 to Oct 23, 2020
Academic Year 2020/2021
Learning outcomes
At the end of the course the student has acquired knowledge of the multivariate methods for analyzing high dimensional data. In particular, he/she is able: - to interpret methods of dimension reduction including principal component analysis and factor analysis - to interpret methods of clustering and discrimination - to apply the proper multivariate method and perform his/her own analysis of high dimensional datasets using the software R.
Course contents
- Multivariate and high dimensional problems. Basics of linear and matrix algebra. Random vectors and Gaussian random vectors.
- Principal component analysis: principal component method, visualising principal components, choosing the number of principal components.
- Factor analysis: factor model specification, identification,estimation, rotation, factor scores .
- Discriminant analysis: linear discriminant analyses, quadratic discriminant analysis, Fisher’s discriminant rule, linear discrimination for two normal populations and classes, evaluation of discriminant rules.
- Cluster analysis: distance and similaty measures, hierarchical Agglomerative Clustering, k-means Clustering
Readings/Bibliography
Koch I. Analysis of Multivariate and High Dimensional Data, Cambridge University Press, 2014
Teaching methods
Lectures and tutorials with the software R
Assessment methods
A final project on real data analysed with the software R on one or combined topics of the course and an oral exam. The oral exam consists of a discussion of the project and theoretical questions.
Teaching tools
Teacher's note available at https://virtuale.unibo.it/
Office hours
See the website of Silvia Cagnone
Alumni