Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
30.9.2019 at 09:00 - 29.10.2019 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Tue 29.10.2019
14:15 - 16:00
Thu 31.10.2019
14:15 - 16:00
Tue 5.11.2019
14:15 - 16:00
Thu 7.11.2019
14:15 - 16:00
Tue 12.11.2019
14:15 - 16:00
Thu 14.11.2019
14:15 - 16:00
Tue 19.11.2019
14:15 - 16:00
Thu 21.11.2019
14:15 - 16:00
Tue 26.11.2019
14:15 - 16:00
Thu 28.11.2019
14:15 - 16:00
Tue 3.12.2019
14:15 - 16:00
Thu 5.12.2019
14:15 - 16:00
Tue 10.12.2019
14:15 - 16:00

Description

The course is intended primarily to Master’s degree statistics students or Ph.D. students in statistics, but it is also suitable for Master’s degree and doctoral students and exchange students from other disciplines with sufficient background in Bayesian statistical inference. The course is also suitable for the 2nd and 3rd year Bachelor degree students of statistics who have taken Bayesian inference course.

Missing data are common in many research problems, for example in health surveys. Missing data mechanisms are often selective, and simple complete-case analyses can yield biased results. This course introduces some commonly applied methods to handle missing data.

Studies in probability, statistical inference, Bayesian inference, data analysis with R.

Contents: The topics covered include

  • types of missing data
  • need to handle missing data
  • techniques to handle different types
  • weighting
  • multiple imputation
  • data augmentation based on Bayesian inference

The course will alternate between lectures (2h/week) and exercises (2h/week).

Scope 5 cr

Tommi Härkänen, Docent