Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
GENERALIZED LINEAR MODELS IN ACTUARIES UAKT403 7. Semester 2 + 2 3.0 6.0

Prerequisites None

Language of Instruction Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery Oral presentation
Course Coordinator
Instructors Furkan BAŞER
Assistants
Goals With this course the students will learn components of the generalized linear models,the estimation algorithm used for generalized linear models, and the most known models in statistics which belong this family and their properties.
Course Content Process in model fitting; The components of a generalized linear models, Measuring the goodness of fit; Residuals, An algorithm for fitting generalized linear models, Models for continuous data with constant variance, Algorithms for least squares for models for continuous data with constant variance, Selection of covariates for models for continuous data with constant variance, Introduction for binary data; Binomial distribution; Models for binary responses, Likelihood function; Over dispersion, Introduction Models for polytomous data; Measurement scales, Multinomial distribution, Likelihood functions; Over dispersion
Learning Outcomes 1) Distinguishing the components of generalized linear models.
2) Obtaining maximum likelihood estimators for this family.
3) Measuring the goodness of fit for these mode
4) Building the models for continuous data
5) Building the models for binary data
6) Building the models for polytomuous data

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Process in model fitting; The components of a generalized linear models Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
2. Week Measuring the goodness of fit; Residual Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
3. Week An algorithm for fitting generalized linear mode Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
4. Week Error structure and Systematic component for models for continuous data with constant variance Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
5. Week Model formulae and aliasing for models for continuous data with constant variance Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
6. Week Estimation for models for continuous data with constant variance; tables as data Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
7. Week Algorithms for least squares for models for continuous data with constant variance Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
8. Week Midterm exam

9. Week Selection of covariates for models for continuous data with constant variance Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
10. Week Introduction for binary data; Binomial distribution; Models for binary responses Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
11. Week Likelihood function; Over dispersion Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
12. Week Introduction Models for polytomous data; Measurement scales Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
13. Week Multinomial distribution Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
14. Week Likelihood functions; Over dispersion Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
15. Week Examples Lecture; Question Answer; Problem Solving

 Presentation (Including Preparation Time)
16. Week Final exam


Sources Used in This Course
Recommended Sources
A. J. Dobson. (2002). “An introduction to generalized linear models”, Chapman&Hall, Boca Raton.
E. F. Vonesh. (2014). “Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS”, SAS Institute Inc., Cary, NC, USA.
McCullagh P, Nelder J. A. , Generalized Linear Models, 2nd ed.,.Chapman and Hall, CRC, 1989.
R. R. Hocking. (1985). "The analysis of linear models", Cole publishing company, Monterey.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5DK6
PY15000000
PY25000000
PY35000000
PY45000000
PY55000000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 4
Work Hour outside Classroom (Preparation, strengthening) 14 4
Homework 3 3
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 2 3
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 24
Final Exam 1 2
Time to prepare for Final Exam 1 24
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload