Actuarial Statistics In general: Fundamentals of Statistics, Linear Algebra, and Mathematical Analysis Some familiarity with programming techniques based on MATLAB, R, and Microsoft Excel is also recommended

Knowledge of Multivariate Data Analysis with applications in Finance and Actuarial Sciences Knowledge of Statistical software for Multivariate Data Analysis, with emphasis in R

The course focuses on Multivariate Data Analysis Techniques, with particular emphasis in computer applications 1. INTRODUCTION ** 1.1 Multivariate data ** 1.2 The data matrix ** 1.3 Summary statistics. Mean vector. Covariance and correlation matrices ** 1.4 Graphical methods ** 1.5 Linear combinations APPENDIX 1A ** 1A.1 Review of basic concepts of vectors and matrices APPENDIX 1B ** 1B.1 Introduction to R. Use of RStudio and RGui in Multivariate Analysis 2. CLUSTER analysis ** 2.1 Distances and similarities ** 2.2 Hierarchical procedures: Ward's method ** 2.3 Nonhierarchical procedures: K-means method ** 2.4 Examples of application with real data 3. POPULATION concepts and SAMPLING ** 3.1 Random vectors and matrices ** 3.2 Expected values ** 3.3 Sampling distributions 4. Multivariate NORMAL distribution ** 4.1 Basic properties ** 4.2 Simulation methods ** 4.3 Examples of application in finance 5. PRINCIPAL components ** 5.1 Motivation and construction ** 5.2 Standardized case ** 5.3 Data example 6. FACTOR analysis ** 6.1 The orthogonal factor model ** 6.2 Estimation and rotation of factors ** 6.3 Examples of application 7. REGRESSION analysis ** 7.1 Simple and multiple regression ** 7.2 Diagnostic and residual analysis ** 7.3 Examples of application 8. GENERALIZED linear models ** 8.1 Logit and probit models ** 8.2 Poisson regression ** 8.3 Multiple choice models ** 8.4 Examples of application

Competences will be acquired by students from: [I] Theory classes: one per week (14 sessions) [eventually online synchronous] [II] Practical classes in the computer room: one per week (14 sessions) [face-to-face] Activities [I] and [II] will be devoted to exercises, problems, data examples, and case studies. Teaching will make intensive use of the resources available in Aula Global. Online sessions will be recorded. Some short reading notes will be also distributed, for helping to understand specific parts of the course and to facilitate its presentation during the lectures.