### Instruction

Name | Cr | Method of study | Time | Location | Organiser |
---|---|---|---|---|---|

High dimensional statistics | 5 Cr | General Examination | 8.1.2020 - 8.1.2020 | ||

High dimensional statistics | 5 Cr | Lecture Course | 29.10.2019 - 15.12.2019 | ||

High dimensional statistics | 5 Cr | General Examination | 7.2.2018 - 7.2.2018 | ||

High dimensional statistics | 5 Cr | Lecture Course | 30.10.2017 - 13.12.2017 |

### Target group

Master's Programme in Mathematics and Statistics (MAST) is responsible for the course.

The course belongs to the Statistics study track of MAST.

The course is available to students from other degree programmes.

### Prerequisites

Basic studies of Bsc level statistics including linear models, linear algebra and R software.

### Learning outcomes

Knowledge of methods for high-dimensional inference problems and practical experience of solving them with computer.

### Timing

Recommended time/stage of studies for completion: 1. or 2. year.

The course is lectured every second year.

### Contents

Statistical inference when either the number of data units is large and/or each unit has been measured on a large number of variables.

1. Large scale inference (P-values, false discovery rates, Bayesian posterior probabilities).

2. Variable selection (stepwise regression, AIC, BIC; penalized regression with glmnet package using Lasso, ridge regression and elastic net).

3. Dimension reduction (principal components analysis, singular value decomposition).

Applications across modern data science.

### Activities and teaching methods in support of learning

Lectures and computer exercises.

### Study materials

Lecture notes during the course.

Selected sections from the books:

James, Witten, Hastie, Tibshirani: Introduction to Statistical Learning

Hastie, Tibshirani, Friedman: Elements of Statistical Learning

### Assessment practices and criteria

Graded 1-5 based on exercises and exam.

### Recommended optional studies

Computational statistics

### Completion methods

Weekly exercises and exam.