Login
English
Nederlands
Contemporary Statistics with Applications (22/23)
| Faculteit | Science and Engineering |
| Jaar | 2022/23 |
| Vakcode | WMMA015-05 |
| Vaknaam | Contemporary Statistics with Applications (22/23) |
| Niveau(s) | master |
| Voertaal | Engels |
| Periode | semester I a |
| ECTS | 5 |
| Rooster | rooster.rug.nl |
| Uitgebreide vaknaam | Contemporary Statistics with Applications (tweejaarlijks 2022/2023) | ||||||||
| Leerdoelen | At the end of the course, the student is able to: 1. analyse data with (penalized) linear regression models, e.g. Lasso and Ridge regression. 2. apply linear classification methods to data, e.g. to perform a linear discriminant analysis (LDA). 3. to implement and to apply cross-validation for model selection, randomization tests for hypothesis testing and Bootstrapping for variance estimation. 4. to implement and to apply the Expectation-Maximization (EM) algorithm to incomplete data, e.g. Gaussian mixture models. 5. to implement and to apply a principal component analysis (PCA) for dimension reduction. 6. to derive the mathematical properties of the discussed statistical methods. |
||||||||
| Omschrijving | This course unit covers a selection of important contemporary statistical modelling techniques for statistical data analyses. The statistical methods will be motivated by applications and/or data examples, before the underlying theoretical concepts will be explained. In practical exercises the methods will be applied to data and the results will be interpreted. The topics of the course include: Penalized linear regression models (e.g. Ridge/Lasso regression), linear classification methods (e.g. LDA), principal component analysis (PCA), Gaussian and other mixture models, the Expectation-Maximisation (EM) algorithm, Cubic splines and Gaussian Process regression, Bootstrap procedures and permutation tests, and hidden Markov models (HMMs). | ||||||||
| Uren per week | |||||||||
| Onderwijsvorm |
Hoorcollege (LC), Opdracht (ASM), Werkcollege (T)
(Weekly twice two-hour lectures and one two-hour practical. The practical will be held in a computer lab and involves programming in R.) |
||||||||
| Toetsvorm |
Opdracht (AST), Verslag (R)
(Assessment takes place through three homework assignments and a research project and report according to the formula: Final grade = max{PR,0.7 x PR + 0.3 x HW} where HW is the average of the three homework assignment grades and PR is the project report grade. The pass mark is: Final grade = 5.5 or higher) |
||||||||
| Vaksoort | master | ||||||||
| Coördinator | prof. dr. M.A. Grzegorczyk | ||||||||
| Docent(en) | prof. dr. M.A. Grzegorczyk | ||||||||
| Verplichte literatuur |
|
||||||||
| Entreevoorwaarden | The course unit assumes prior knowledge acquired from the two course units Probability Theory and Statistics (from the Mathematics BSc programme). | ||||||||
| Opmerkingen | This course was registered last year with course code WICSA-10 | ||||||||
| Opgenomen in |