| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Bioinformatics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| second year Master Biostatistics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Data Science - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Quantitative Epidemiology - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- 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 capable of acquiring new knowledge. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | - 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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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.
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Semiparametric regression methods build on parametric regression models by allowing more flexible relationships between the predictors and the response variables. Examples of semiparametric regression 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 the techniques 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 in semiparametric regression. There will be a strong emphasis on implementation in R with a lot of computing exercises.
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Collective feedback moment ✔
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Distance learning ✔
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Homework ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 40 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Oral evaluation during teaching period | 25 % |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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The R software will be used in this course. |
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| Recommended reading |
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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 |
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| Recommended course material |
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"Semiparametric Regression" by D. Ruppert, M. Wand and R. Carroll, CambridgeUniversity Press (2003)
"Generalized Additive Models: An Introduction with R" by S. Wood, Chapman and Hall/CRC, 2nd edition (2017) |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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