Intro
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.
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.
Forkunnskapskrav
Ingen
Anbefalte forkunnskaper
A master's degree in statistics, or a related subject which includes a variety of statistics courses.
Method of work
Lectures and guided self-study.
Åpent for
Technology and Natural Science - PhD programme
Emneevaluering
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.
The course description is retrieved from FS (Felles studentsystem). Version 1