Quantitative Finance and Insurance

Statistics II

Statistics II

Academic year 2013/2014

Sommario del corso

• Course objectives
• Results of learning outcomes
• Learning assessment methods
• Program
• Suggested textbooks and readings
• Notes
• Teaching tools

Course objectives

The course aims at introducing the bases of multivariate analysis and nonlinear statistical modeling. Main topics are principal component analysis, cluster analysis, canonical correlation and generalize linear models (GLM) with particular emphasis on logistic regression and log-linear models.

Results of learning outcomes

This course will provide students with the fundament of statistical modelling. Students will learn how to take real world phenomena with suitable statistical tools and models. Estimation and testing techniques will be introduced and particular emphasis will be devoted to the interpretation of the output of the analysis, in order to transform empirical evidence into knowledge. The course will give to students the suitable basis for further studies and applications of Statistics. 

Learning assessment methods

Written exam

Program

Multivariate analysis:

Principal Component Analysis
Cluster Analysis
Canonical Correlation

Nonlinear Statistical Models:

Review of the linear model and maximum likelihood estimation
Generalize linear models (GLM)
Inference in GLM
Goodness of fit: deviance and residual analysis
Typical problems: logistic regression, Poisson regression and log-linear models

- Lecture notes of the instructors

- Azzalini, A. (1996). Statistical Inference Based on the likelihood, Chapman and Hall
- Rencher, A. (2012). Methods of Multivariate Analysis (3rd ed.), Wiley
- Afifi A., Clark V.A. (1996). Computer-aided Multivariate Analysis (3rd ed.), Chapman & Hall

Note

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums