- Docente: Angela Montanari
- Credits: 6
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
-
Corso:
Second cycle degree programme (LM) in
Statistical Sciences (cod. 9222)
Also valid for First cycle degree programme (L) in Statistical Sciences (cod. 8873)
Learning outcomes
By the end of the course the student gains an appreciation of the types of problems and questions arising with multivariate data. In particular the student should be able: - to apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, factor analysis, canonical variates - to apply and interpret methods for cluster analysis and discrimination - to interpret the output of R procedures for multivariate statistics
Course contents
Multivariate data and derived matrices. Dimension reduction methods: principal component analysis, factor analysis. Cluster analysis. Discriminant analysis
Readings/Bibliography
Handnotes on IOL
In Italian
-Appunti di analisi statistica multivariata, S.Mignani e A.Montanari, Esculapio
In English
http://en.bookfi.org [http://en.bookfi.org/]
-Applied Multivariate Data Analysis. B.S. Everitt and G.G. Dunn, 2001, Edward
Arnold. Second Edition.
-A First Course in Multivariate Statistics. B. Flury, 1997, Springer Verlag.
-Introduction to Multivariate Analysis. C. Chatfield and A.J. Collins, 1980, Chapman
and Hall.
-Principles of Multivariate Analysis: A User's Perspective. W.J. Krzanowski, 1988,
Oxford University Press.
-Multivariate Analysis. K.V. Mardia, J.T. Kent and J. Bibby, 1979, Academic Press.
-Modern Applied Statistics with S. Venables, W.N. and Ripley, B.D. 2002. 4th
Edition. Springer Verlag, New York. Very useful for Splus and R
-The Elements of Statistical Learning. Trevor Hastie, Robert Tibshirani and Jerome
Friedman. Springer Verlag, New York. Some very advanced material as well as that
covered in this course (but available for free as a .pdf download on http://www.stat.
stanford.edu/~tibs/ElemStatLearn/)
Practicals
Handnotes on IOL
Teaching methods
lectures and practicals
Assessment methods
written exam
see http://www2.stat.unibo.it/montanari/course3.htm for past exams
Office hours
See the website of Angela Montanari