Module MA4962-KP05
Generalized Linear Models (VLModKP05)
Duration
1 Semester
Turnus of offer
irregularly
Credit points
5
Course of studies, specific fields and terms:
- Master CLS 2023, optional subject, mathematics
- Bachelor CLS 2023, optional subject, mathematics
- Bachelor CLS 2016, optional subject, mathematics
- Master CLS 2016, optional subject, mathematics
Classes and lectures:
- Generalized Linear Models (exercise, 1 SWS)
- Generalized Linear Models (lecture, 2 SWS)
Workload:
- 15 hours exam preparation
- 30 hours programming
- 45 hours in-classroom work
- 60 hours private studies
Contents of teaching:
- General overview of generalized linear models (GLM): - link and response function, - GLM algorithms: Newton-Raphson, Fisher Scoring, iterated weighted least squares, - convergence, - quality of the adaption, - residuals
- Continuous response models: Gaussian, log-normal, Gamma, log-Gamma for survival analysis, inverse Gaussian
- Dichotomous response models: logit, probit, cloglog
- Count data: Poisson, negative binomial with over- and underdispersion
- Ordinal response models: proportional odds model
- Disordered categorial response models: Multinomial logit and probit model
- Censored continuous response models: Tobit model
Qualification-goals/Competencies:
- The students are able to explain the theoretical bases of generalized linear models (GLM).
- They are able to explain areas of application for GLM.
- They are able to select a suitable GLM.
- They are able to estimate GLMs in R.
- They are able to explain the R source code in a presentation.
- They are able to judge the results of GLMs in R critically.
- They are able to evaluate algorithmic challenges of GLMs.
- They are able to explain conceptual problems of GLMs for categorial response variables.
- They are able to implement GLM in R.
- They are able to apply regression diagnostics to GLMs and to judge the results.
- They are able to describe the most important estimation algorithms for GLMs.
- They are able to list the statistical properties of GLMs.
Grading through:
- Viva Voce or test
Requires:
Responsible for this module:
- Prof. Dr. rer. biol. hum. Inke König
Teacher:
- Institute of Medical Biometry and Statistics
- Prof. Dr. rer. biol. hum. Inke König
Literature:
- Agresti, Alan : Foundations of Linear and Generalized Linear Models Wiley, 2015
Language:
- English, except in case of only German-speaking participants
Notes:
Admission requirements for taking the module:- None (The competencies of the modules listed under 'Requires' are needed for this module, but are not a formal prerequisite)
Admission requirements for participation in module examination(s):
- Successful completion of homework assignments as specified at the beginning of the semester
Module exam(s):
- MA4962-L1: Generalized Linear Models, written exam (90 min) or oral exam (30 min), 100 % of module grade
Last Updated:
22.02.2022