Entry requirements

120 credits including Analysis of Regression or Introduction to Data Science. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

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.

On completion of the course, 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 statistical smoothing methods;
• use statistical software. 

Content

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, preferably R.

Instruction

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

Assessment

Written examination (4 credits) at the end of the course. Compulsory assignments (1 credit) and projects (2.5 credits) during the course.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university..

Other directives

This course cannot be included in the same degree as the course ​Computer-Intensive Statistics and Data Mining (1MS009).