Introduction to Semiparametric Regression (4004)
  
Coordinating lecturer :Prof. dr. Tomasz BURZYKOWSKI 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Prerequisites

The student is familiar with linear regression and with the statistical computing environment R.

Students who are uncertain about their level of preparation are encouraged to contact the instructor.


Content

Semiparametric regression methods build on parametric regression models by allowing more flexible relationships between the predictors and the response variables. Examples of semiparametricregression include generalized additive models, additive mixed models and spatial smoothing. Our goal is to provide an easy-to-follow applied course on semiparametric regression methods using R.

There is a vast literature on the semiparametric regression methods. However, most of it is geared towards researchers with advanced knowledge of statistical methods. This course explains thetechniques and benefits of semiparametric regression in a concise and modular fashion. Spline functions, linear mixed models and hierarchical models are shown to play an important role insemiparametric regression. There will be a strong emphasis on implementation in R with a lot ofcomputing exercises.


Organisational and teaching methods
Organisational methods  
Collective feedback moment ✔  
Distance learning ✔  
Teaching methods  
Homework ✔  
Paper ✔  
Presentation ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written evaluation during teaching period40 %
Transfer of partial marks within the academic year✔
Homework✔
Oral evaluation during teaching period25 %
Presentation✔
Written exam35 %
Paper✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

The R software will be used in this course. 
 

Recommended reading
  Semiparametric Regression with R,J. Harezlak, D. Ruppert, and M.P. Wand,Springer,Available as e-book: https://link.springer.com/book/10.1007%2F978-1-4939-8853-2
 

Recommended course material
 

"Semiparametric Regression" by D. Ruppert, M. Wand and R. Carroll, Cambridge University Press (2003)

"Generalized Additive Models: An Introduction with R" by S. Wood, Chapman and Hall/CRC, 2nd edition (2017)


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Biostatistics J
2nd year Master Data Science J
2nd year Master Quantitative Epidemiology J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.