General statistical methods (STA903)
Depending on the composition of the PhD candidate group, the following topics will be covered with varying degree of depth: statistical inference theory, asymptotic theory, computational statistical methods, robust estimation and non-parametric methods. Further, selected topics in time series analysis, stochastic processes, survival analysis, spatial statistics or statistical methods on manifolds.
Course description for study year 2025-2026
Facts
Course code
STA903
Version
1
Credits (ECTS)
10
Semester tution start
Spring, Autumn
Number of semesters
1
Exam semester
Spring, Autumn
Language of instruction
English, Norwegian
Content
Depending on the composition of the PhD candidate group, the following topics will be covered with varying degree of depth: statistical inference theory, asymptotic theory, computational statistical methods, robust estimation and non-parametric methods. Further, selected topics in time series analysis, stochastic processes, survival analysis, spatial statistics or statistical methods on manifolds.
Learning outcome
After completing the course, the candidate should have acquired knowledge regarding central concepts and ideas within advanced statistical theory and applications of such theory. The candidate should be able to apply such knowledge to understand advanced statistical texts and as a tool in their own research.
Required prerequisite knowledge
None
Recommended prerequisites
A master's degree in statistics, or a related subject which includes a variety of statistics courses.
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Oral exam | 1/1 | Passed / Not Passed |
Course teacher(s)
Course coordinator:
Tore Selland KleppeCourse teacher:
Jörn SchulzCourse coordinator:
Jan Terje KvaløyHead of Department:
Bjørn Henrik AuestadMethod of work
Lectures and guided self-study.
Open for
Course assessment
The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.
