Course type: compulsory
Lecturer: Aleš Žiberna, Ph.D., Associate Professor
 

For the timetable see Curriculum.

Prerequisites:

• Enrollment in study year.

Content (Syllabus outline):
Monte Carlo methods:

• Basic characteristics
• Uses:
  • estimating standard errors
  • hypothesis testing
  • testing statistical methods

Bootstrap:

• Basic characteristics
• Uses:
  • estimating standard errors
  • hypothesis testing
• Estimating and correcting bias
• Expansions

Permutation tests:

• Basic characteristics
• Assumptions
• Hypothesis testing

Model validation:

• "Jackknife"
• Cross-validation

Missing values:

• Types and mechanisms of missing values
• Methods for dealing with missing values:
  • Multiple imputations
  • EM algorithm

Objectives and competences:
The aim of the course is to enable the students for learning, adapting and using computer intensive methods in statistics. After the course students should be able to use these methods for solving real statistical problems that cannot be solved analytically.

Intended learning outcomes:
Students learn selected computer intensive methods and understand basic principles of these methods. They are able to use them on problems disused in the class and to adapt and use them on similar problems.