Syllabus for Theoretical Statistics
A revised version of the syllabus is available.
- 10 credits
- Course code: 1MS033
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2012-03-08
- Established by: The Faculty Board of Science and Technology
- Applies from: Autumn 2012
120 credit points including 90 credit points of Mathematics. Real Analysis recommended.
- Responsible department: Department of Mathematics
In order to pass the course the student should be able to
- understand the principles of optimal estimation;
- understand the theory of optimal tests, especially unbiased and invariant tests;
- describe the decision theory;
- understand the principles of the asymptotic behaviour of statistical methods, especially the asymptotic efficiency;
- using the delta method , including the functional delta method;
- understand the use of projection in statistics especially in linear regression and variance analysis.
Maximum likelihood estimator, James Stein estimator, M-estimators, optimality of the F-test, minimax tests, asymptotic efficiency, LAN model, U statistics, Hajek projection, linear models
Lectures and problem solving sessions.
Written examination at the end of the course combined with compulsory assignments. The assignments can be registered as part of the written examination.
- Latest syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Spring 2019)
- Previous syllabus (applies from Autumn 2016)
- Previous syllabus (applies from Autumn 2012, version 2)
- Previous syllabus (applies from Autumn 2012, version 1)
Applies from: Autumn 2012
Some titles may be available electronically through the University library.
van der Vaart, A. W.
Cambridge University Press, 2000
Introduction to the theory of statistical inference
Boca Raton, FL: CRC Press, 2012