Study-unit STATISTICAL METHODS FOR ECONOMY AND FINANCE
| Course name | Finance and quantitative methods for economics |
|---|---|
| Study-unit Code | A000201 |
| Location | PERUGIA |
| Curriculum | Statistics for finance and economics |
| Lecturer | Luca Scrucca |
| CFU | 12 |
| Course Regulation | Coorte 2018 |
| Supplied | 2018/19 |
| Type of study-unit | Obbligatorio (Required) |
| Type of learning activities | Attività formativa integrata |
| Partition |
STATISTICAL LEARNING AND DATA MINING
| Code | A000203 |
|---|---|
| Location | PERUGIA |
| CFU | 6 |
| Lecturer | Luca Scrucca |
| Lecturers |
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| Hours |
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| Learning activities | Caratterizzante |
| Area | Matematico, statistico, informatico |
| Sector | SECS-S/01 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | English |
| Contents | Advanced statistical methods for Data Mining, both supervised (classification and regression) and unsupervised (clustering and dimension reduction). Real data case studies are introduced and analysed using the software R. |
| Reference texts | James G., Witten D., Hastie T. and Tibshirani R. (2013) An Introduction to Statistical Learning with Applications in R. Springer-Verlag. Supplemental material will be provided by the instructor during the course. |
| Educational objectives | Upon completion of this course the student should be able to apply independently the appropriate statistical methods to real regression, classification, and clustering problems, thorough the use of the software R. |
| Prerequisites | A basic knowledge of statistics, both descriptive and inference, and the linear regression model. |
| Teaching methods | Lectures and practical sessions in the computer lab. |
| Other information | Attending classes is not mandatory but strongly advised. |
| Learning verification modality | Progress assessments and final oral exam. The computer laboratory activities are aimed to assess the student's ability to put into practice the methods introduced in the classroom. Final oral examination instead intends to assess the level of knowledge and understanding achieved by the student regarding the computational and methodological aspects covered during the course. |
| Extended program | The course aims at presenting advanced statistical methods for Data Mining, both supervised (classification and regression) and unsupervised (clustering and dimension reduction). These methods have been successfully applied in many fields, from finance to economy, from business analytics to natural and social sciences. Real data case studies will be introduced and analysed using the statistical software R. Specifically, the following topics will be covered: - Statistical learning and data mining. - Prediction vs interpretation. - Supervised vs unsupervised learning. - Classification vs regression. - Evaluating the accuracy of a statistical model. - Supervised learning: introduction. - Extensions to the linear model: model selection and regularisation. Polynomial regression, splines, generalised additive models. - Resampling methods: cross-validation and bootstrap. - Classification: introduction. - Logistic model and multinomial model. - Linear and quadratic discriminant analysis. - k-nearest neighbour algorithm. - Tree-based methods: bagging, random forests, boosting. - Unsupervised learning: introduction. - Principal component analysis. - Similarity measures and distance matrix. - Cluster analysis: hierarchical methods. - Non-hierarchical methods: k-means. - Model-based clustering. |
STATISTICAL METHODS FOR FINANCE (MOD.I)
| Code | A000202 |
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| Location | PERUGIA |
| CFU | 6 |
| Lecturer | Silvia Bacci |
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| Hours |
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| Learning activities | Caratterizzante |
| Area | Matematico, statistico, informatico |
| Sector | SECS-S/01 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | English |
| Contents | STATISTICAL INFERENCE: multivariate random variables and hypothesis testing LINEAR REGRESSION: model assumptions, parameter estimation, parameter interpretation, goodness of fit, prediction, residual analysis, violations of basic assumptions |
| Reference texts | R. A. Johnson e D. W. Wichern, Applied Multivariate Statistical Analysis, 6th edition, Prentice Hall, New Jersey, 2007. S. J. Sheather, A Modern Approach to Regression with R, Springer Texts in Statistics, 2009. |
| Educational objectives | Knowledge and ability to apply methods of statistical inference and multiple linear regression to real data through the use of software R |
| Prerequisites | Fundamentals of statistics (descriptive statistics and statistical inference) |
| Teaching methods | Lectures and laboratory sections with R software |
| Learning verification modality | The exam consists in a practical test with software R and in a written test with open questions about theoretical topics. |
| Extended program | Probability distributions: multivariate random variables, multivariate normal distribution, Chi-squared, Student’s t, Fisher’s F. Statistical inference: maximum likelihood approach, hypothesis testing on one or more parameters. Simple linear regression: model definition, parameter estimation: least squares method and maximum likelihood method, hypothesis testing, residual analysis. Multiple linear regression: model assumptions, parameter interpretation, parameter estimation: least squares method and maximum likelihood method, interval estimation, hypothesis testing, categorical explicative variables, analysis of variance, goodness of fit, model selection methods, residual analysis, multicollinearity. Logit regression: model assumptions, parameter interpretation, parameter estimation. |