Module Guide from winter term 2019/20

Modul MA4962-KP05

Generalized Linear Models (VLModKP05)


1 Semester
Turnus of offer:

Credit points:

Course of studies, specific field and terms:
  • Master CLS 2023 (optional subject), mathematics, 1st, 2nd, or 3rd semester
  • Bachelor CLS 2023 (optional subject), mathematics, 5th and 6th semester
  • Bachelor CLS 2016 (optional subject), mathematics, 5th or 6th semester
  • Master CLS 2016 (optional subject), mathematics, 1st, 2nd, or 3rd semester
Classes and lectures:
  • Generalized Linear Models (exercise, 1 SWS)
  • Generalized Linear Models (lecture, 2 SWS)
  • 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
  • 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
Responsible for this module:
  • Prof. Dr. rer. biol. hum. Inke König
  • Prof. Dr. rer. biol. hum. Inke König
  • Agresti, Alan: Foundations of Linear and Generalized Linear Models - Wiley, 2015
  • English, except in case of only German-speaking participants

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

Letzte Änderung: