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
- Revised: 2016-04-22
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2016
120 credits including 90 credits of Mathematics. Inference Theory II.
- Responsible department: Department of Mathematics
In order to pass the course the student should be able to
- explain the principles of optimal estimation;
- explain the theory of optimal tests, especially unbiased and invariant tests;
- give an account of the decision theory;
- explain the principles of the asymptotic behaviour of statistical methods, especially the asymptotic efficiency;
- use the delta method, including the functional delta method;
- explain 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 (8 credits points) at the end of the course as well as assignments (2 credit points) in accordance with instructions at course start.
Applies from: Autumn 2016
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