Theoretical Statistics (TeoStat)

Course content

The course presents principles underlying statistical inference and provides tools for analyzing statistical methodology. It connects classical statistical theory such as the maximum likelihood principle or the analysis of unbiased estimators, to modern statistical methods, such as kernel machines and high-dimensional statistics.

Education

MSc Programme in Statistics

Learning outcome

Knowledge:

  • Maximum likelihood principle
  • distances between distributions
  • unbiased estimators, completeness
  • reproducing kernel Hilbert spaces
  • support vector machines
  • LASSO


Skills: 

  • using linear algebra and functional analysis for statistical analysis
  • ridge penalties
  • concentration inequalities


Competences:

  • theoretical analysis and evaluation of statistical methods
  • developing of new statistical methodology

4 hours lectures and 4 hours of exercises per week for 7 weeks.

See Absalon for a list of course literature.

 

MI, Stat1, Stat2 or similar. Relevant concepts from functional analysis will be introduced in the course.

ECTS
7,5 ECTS
Type of assessment
Oral examination, 25 minutes
There will be a 30min preparation time before the oral exam.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Theory exercises
  • 28
  • Preparation
  • 115
  • Exam
  • 35
  • English
  • 206