- Docente: Silvia Cagnone
- Crediti formativi: 6
- SSD: SECS-S/01
- Lingua di insegnamento: Italiano
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea Magistrale in Economics (cod. 8408)
Conoscenze e abilità da conseguire
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.
Contenuti
- 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
Testi/Bibliografia
Koch I. Analysis of Multivariate and High Dimensional Data, Cambridge University Press, 2014
Metodi didattici
Lectures and tutorials with the software R
Modalità di verifica e valutazione dell'apprendimento
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.
Strumenti a supporto della didattica
Teacher's note available at https://virtuale.unibo.it/
Orario di ricevimento
Consulta il sito web di Silvia Cagnone