NMAA13025U Theoretical Statistics (TeoStat)

Volume 2018/2019
Education

MSc Programme in Statistics

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.

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
Literature

See Absalon for a list of course literature.

 

MI, Stat1, Stat2 or similar. Relevant concepts from functional analysis will be introduced in the course.
4 hours lectures and 4 hours of exercises per week for 7 weeks.
  • Category
  • Hours
  • Exam
  • 35
  • Lectures
  • 28
  • Preparation
  • 115
  • Theory exercises
  • 28
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Oral examination, 25 minutes
There will be a 30min preparation time before the oral exam.
Exam registration requirements

The students have to hand-in 3 group assignments (up to two students), which need to get approved. 

Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Same as ordinary exam. If the mandatory assignments have not been approved during the course the non-approved assignment(s) must be handed in no later than two weeks before the beginning of the re-exam week. The assignments must be aproved before the re-exam.

Criteria for exam assesment

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