Statistics B

Course description

Content

The course covers a number of modern statistical models and methods, mathematical methods for analyzing them, and mathematical relations between the different methods.

The course will cover the following content:

• Elements of statistical decision theory

• Regularization for high-dimensional and non-parametric regression

• Kernel methods and reproducing Hilbert space theory

• Concentration inequalities and their relation to finite sample error bounds

• Sparsity and high-dimensional theory

The focus of this course is on the mathematical foundations of modern statistical methods. The content will be presented with a focus on statistical guarantees that can be achieved with these methods.

Recommended academic qualifications

Probability theory and mathematical statistics equivalent to the courses Measure and Integrals and StatMet and MStat (alternatively “MatStat” from previous years) Linear Algebra at least at the level of the BSc course LinAlgMat (NMAB10006U). Knowledge of conditional distributions as covered in either Statistics A or Graphical Models from previous years.

It is recommended that the course Regression is taken prior to this course.

Academic qualifications equivalent to a BSc degree is recommended.