Entry requirements

120 credit points including Analysis of Regression and Variance or corresponding course

Learning outcomes

In order to pass the course (grade 3) the student should be able to

• give an account for the theoretical foundation of Markov Chain Monte Carlo-methods and to use such techniques to solve given statistical problems;
• give an account for the principles behind random number generators;
• use simulation methods such as Bootstrap and SIMEX;
• use EM methods;
• use non-parametric statistical models;
• use statistical software, preferably R.

Content

The purpose of the course is to give the student a good overview about statistical techniques that have been developed during the last years due to increasing computer capacity. Resampling techniques, Jack-knife, bootstrap. . EM algorithms. SIMEX methodology. Markov Chain Monte Carlo (MCMC) methods. Random number generators. Smoothing techniques. Kernel estimators, nearest neighbour estimators, orthogonal and local polynomial estimators, wavelet estimators. Splines. Choice of bandwidth and other smoothing parameters. Applications. Use of statistical software.

Instruction

Lectures, problem solving sessions and computer-assisted laboratory work.

Assessment

Written examination at the end of the course combined with compulsory assignments and oral presentation according to directions given at course start.