Topics in Statistics

Course content

The purpose of this course is to introduce the student to the theoretical analysis of nonparametric statistical methods. The essence of the course is several mathematical results both on what is possible and impossible using nonparametric regression and nonparametric hypothesis testing.

The course will cover

  • Nonparametric regression methods
  • Minimax lower bounds for nonparametric regression
  • Nonparametric hypothesis testing
  • Impossibility results for nonparametric hypothesis testing

MSc Programme in Statistics
MSc Programme in Mathematics-Economics

Learning outcome


  • Uniform Type I and II error control for nonparametric hypotheses
  • Error bounds for nonparametric regression estimators under smoothness assumptions
  • Nonparametric unconditional and conditional independence testing
  • Methods for nonparametric regression including their advantages and disadvantages
  • Results on the fundamental limits of nonparametric statistics


Skills: Ability to

  • prove upper and lower bounds for a nonparametric regression problem
  • theoretically analyze a nonparametric hypothesis testing problem


Competences: Ability to

  • assess whether a nonparametric statistical hypothesis is testable
  • determine whether a nonparametric regression method is optimal for a given distribution
  • give an oral presentation of a specific topic within the theory covered by the course

4 hours of lectures and 3 hours of exercises per week for 7 weeks.

Experience with theoretical statistics at the level of Statistics B and measure theoretic probability (e.g. at the level of Sand and Sand2).

It is advantageous to also have done Regression and Statistics A to fully appreciate the results of the course.

7,5 ECTS
Type of assessment
Oral examination, 30 minutes (30-minute preparation time)
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Same as the ordinary exam

Criteria for exam assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 156
  • Exercises
  • 21
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Anton Rask Lundborg   (3-657670447165786c326f7932686f)
Saved on the 14-02-2024

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