NSCPHD1209 Non-Parametric Statistical Methods in Insurance
Volume 2013/2014
Education
MSc Programme in Actuarial
Science
Content
Basic theory of counting
processes, martingale methods,
estimation with truncated and censored data, Kaplan-Meier estimation with asymptotic properties, Cox regression, kernel methods, nonparametric regression.
estimation with truncated and censored data, Kaplan-Meier estimation with asymptotic properties, Cox regression, kernel methods, nonparametric regression.
Learning Outcome
Knowledge: A good
knowledge of the theory and practical use of nonparametric methods
in insurance, with particular emphasis on non-life insurance. In
particular be able to:
Identify the difference between truncated and censored data, and how they are treated statistically
Use martingale methods to estimate hazard rates
Use non parametric likelihood to estimate hasard rates
Know the Kaplan-Meier estimates and their asymptotic properties
Know how the Cox regression can be deducted by non parametric likelihood
Know how densities and hazard rates can be smoothed by kernel methods
Have some knowledge about non parametric regression
Use R to solve practical problems
Skills: To be able to understand and apply nonparametric methods in practice
Competences: Have a good insight into various nonparametric methods and the application of these
Identify the difference between truncated and censored data, and how they are treated statistically
Use martingale methods to estimate hazard rates
Use non parametric likelihood to estimate hasard rates
Know the Kaplan-Meier estimates and their asymptotic properties
Know how the Cox regression can be deducted by non parametric likelihood
Know how densities and hazard rates can be smoothed by kernel methods
Have some knowledge about non parametric regression
Use R to solve practical problems
Skills: To be able to understand and apply nonparametric methods in practice
Competences: Have a good insight into various nonparametric methods and the application of these
Academic qualifications
A good statistical
background, for example Non-life 2 in the 2013 or 2014
version
Teaching and learning methods
5 hours of lectures per week
for 7 weeks
Workload
- Category
- Hours
- Course Preparation
- 135
- Exam
- 3
- Lectures
- 28
- Project work
- 40
- Total
- 206
Sign up
Please register at:
jostein@math.ku.dk
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written examination, 3 hours under invigilationThe exam consists of problems that can be solved using the theory taught in the class
- Exam registration requirements
- Two mandatory assignments must be approved and valid before the
student is
allowed attending the exam. - Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
- Re-exam
- 30 minutes oral exam with several external examiners. No preparations. The two mandatory assignments must be approved and valid
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NSCPHD1209
- Credit
- 7,5 ECTS
- Level
- Ph.D.
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- B
- Course capacity
- No limit.
- Continuing and further education
- Study board
- Natural Sciences PhD Committee
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Jostein Paulsen (jostein@math.ku.dk)
Phone +45 35 32 07 37 office
04.3.19
Saved on the
24-09-2013