This course gives an overview of statistical methods that are used for analyzing high- dimensional data sets in which many variables (often thousands) have been measured for a limited number of subjects. This type of data arises in genomics, where genetic information is measured for many thousands of genes simultaneously, but also in functional MRI imaging of the brain. The course covers the most important statistical issues in this field, which include: a) initial processing of the data; b) model- based differential expression analysis for Gaussian and count data (classical and Bayesian methods); c) multiple testing (family-wise error rate and false discovery rate control); d) penalized regression (lasso and ridge); e) shrinkage; and f) graphical models for constructing networks. Several specific types of high-dimensional data will be discussed and used during the course. Philosophy: Teaching students the adjustments to classical statistical methodology, necessary to tackle high-dimensional data.
Students should be able to perform and understand the most common analysis types on several types of high-dimensional data, and be familiar with the specific issues in important types of high dimensional data sets.
Mode of Instruction
The course consists of a series of lectures and practicals (partly computer practicals, partly exercises).
For the course days, course location and class hours check the Time Table 2013-14 under the tab “Masters Programme” at http://www.math.leidenuniv.nl/statscience
- written exam
Written exam date is on 20th of January 2014 from14.00 to 17.00 (room is tba), the resit is scheduled for the 28th of February 2014 from 14.00 to 17.00 (room is tba).
Literature will be specified during course, no books are required.
Besides the registration for the (re-)exam in uSis, course registration via blackboard is compulsory.
Exchange and Study Abroad students, please see the Prospective students website for information on how to apply.
- This is an elective course in the Master’s programme of the specialisation Statistical Science for the Life & Behavioural sciences.