Statistics, Computational Statistics, Second Cycle, 7.5 credits

Learning outcomes

Knowledge and understanding

After completing the course the student shall have

• knowledge of numerical methods and their limitations
• knowledge of common computationally intensive methods for statistical analysis.

Competence and skills

The should shall after completing the course be able to

• independently implement computational algorithms.

Judgement and approach

After completing the course the student has the ability to

• independently adapt and select an appropriate algorithm based on the requirements of the statistical issue
• independently seek new knowledge and judge its relevance for the statistical issue at hand
• independently design simulation studies for evaluating the statistical properties of a test or estimator.

Content

• Basic concepts in numerical analysis
• Numerical optimization, linear algebra and integration
• Random number generation
• Monte Carlo simulation and variance reduction
• Bootstrap and Jackknife
• MCMC methods.

Examinations and grades

Written Examination, 6 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Assignments, 1.5 credits (Code: A002)
Grades used are Fail (U) or Pass (G).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

To obtain a passing grade for the course as a whole, a passing grade is required on all course components. The final grade for the entire course is a function of the grades of the course components. Detailed information on the requirements for different grade levels is given at the course start.

Modes of assessment

Written Examination, 6 credits (Code: A001)

Assignments, 1.5 credits (Code: A002)
Oral and written presentation.

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

First-cycle courses of 90 credits in Statistics, alternatively first-cycle courses of 30 credits in statistics and 60 credits in mathematics, alternatively first-cycle courses of 60 credits in statistics including 7.5 credits of Statistical theory and 7.5 credits of Regression analysis/Econometrics. The applicant must also have the course Computer Science, Programming for Statisticians, Second Cycle, 5 credits and qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations.

Other provisions

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Required Reading

Givens, G. H. & Hoeting, J. A. (latest edition)
Computational Statistics
Wiley