AKT704 - ACTUARIAL DATA GRADUATION
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ACTUARIAL DATA GRADUATION | AKT704 | 2nd Semester | 3 | 0 | 3 | 10 |
| Prequisites | - | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Drill and Practice | |||||
| Instructor (s) | Department Instructors | |||||
| Course objective | The aim of the course is to introduce statistical techniques used in actuarial data graduation including generalised linear models with an emphasis on practical considerations. | |||||
| Learning outcomes |
| |||||
| Course Content | Graduation process, testing smoothness, testing adherence to data, graphical graduation, graduation by reference to a standard table, graduation by mathematical formula, generalised linear models: assumptions, exponential family, Newton-Raphson method, deviance, residual analysis, model statistics, continuous response variables, discrete response variables, Gompertz-Makeham family, the problem of duplicates. | |||||
| References | 1. Forfar D.O, Mccutcheon, J.J., Wilke, A.D. (1988), On graduation by mathematical formula, Journal of the Institute of Actuaries, 115, I, 1-149. 2. McCullagh, P., Nelder, J. A. (1989), Generalized linear models, Chapman & Hall 3. Dobson, A. J. (2001), An Introduction to Generalized Linear Models, Chapman & Hall. 4. Fox, J. (2002), An R and S-PLUS Companion to Applied Regression. Sage Publications. 5. Lecture notes | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction |
| Week 2 | Graduation process, examples of poor graduation, testing smoothness, application in R |
| Week 3 | Testing adherence to data, chi-square test, the standardized deviations test, a test for bias, change of sign test, cumulative deviations test, the grouping of signs test, serial correlation test, application in R |
| Week 4 | Graphical graduation, graduation by reference to a standard table, application in R |
| Week 5 | Graduation by mathematical formula, introduction to generalised linear models (GLM) |
| Week 6 | GLM: Assumptions, exponential family, Newton-Raphson method |
| Week 7 | Midterm Exam |
| Week 8 | GLM: Deviance, residual analysis, model statistics |
| Week 9 | GLM: The Gaussian family, the Gamma family, the inverse Gaussian family |
| Week 10 | GLM: The Poisson family, the negative binomial family |
| Week 11 | Application in R |
| Week 12 | Gompertz-Makeham family, the problem of duplicates, application in R |
| Week 13 | Presentation of project |
| Week 14 | Presentation of project |
| Week 15 | Preparation for Final Exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 10 | 10 |
| Presentation | 0 | 0 |
| Project | 1 | 30 |
| Seminar | 0 | 0 |
| Midterms | 1 | 10 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 12 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 48 | 48 |
| Homework assignment | 10 | 7 | 70 |
| Midterms (Study duration) | 1 | 30 | 30 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 41 | 133 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Develop new strategies and techniques in modelling actuarial problems and produce solutions specific to a problem. | X | ||||
| 2. Conduct detailed research in a specific subject in actuarial science area. | X | ||||
| 3. Have the required level of competency in actuarial sciences to be able to make contribution to the actuarial literature. | X | ||||
| 4. Able to use the knowledge in actuarial sciences in multidisciplinary studies. | X | ||||
| 5. Arrange events and projects in actuarial sciences . Able to conduct the stages of designing, executing and reporting results of a project. | X | ||||
| 6. Have scientific scepticism. | X | ||||
| 7. Able to produce scientific publications in the area of actuarial science. | X | ||||
| 8. Able to think analytically. | X | ||||
| 9. Follow national and international innovations and improvements in the area. | X | ||||
| 10. Follow actuarial literature | X | ||||
| 11. Improve foreign language skills in order to do work and presentation in that language. | X | ||||
| 12. Use information technology in an advanced level. | X | ||||
| 13. Able to work individually and have the ability to decide independently. | X | ||||
| 14. Have the qualifications necessary for a team work and able to be the team leader. | X | ||||
| 15. Have the consciousness of professional and social responsibility. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest