| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Group 1 |
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For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
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Software and Data Management DL (3578)
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5,0 stptn |
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Or group 2 |
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For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
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Programming in R DL (4432)
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3,0 stptn |
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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 | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| second year Master Biostatistics - distance learning | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - 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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Simulations, Monte Carlo methods, Bootstrap techniques, Randomization methods, Permutation methods.
Aims
At the end of this course, the student Should Be Able To :
- describe basic simulation methods
- implement basic thesis simulation methods in R
- write programming code in R to run simulations
- discuss the basic concepts to the bootstrap method
- explain the different steps of the bootstrap algorithm
- Explain the difference between parametric, semiparametric and fully nonparametric bootstrap approaches
- apply the bootstrap for hypothesis testing
- explain, and compute different bootstrap confidence intervals
- apply the bootstrap on linear models, taking into account different study designs
- explain and apply randimization and permutation methods
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Distance learning ✔
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Project ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Oral exam | 50 % |
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| Other | Questions about the content/materials of the final project |
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| Other exam | 50 % |
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| Other | Written project (in three parts). |
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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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| Recommended reading |
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- Bootstrap Methods and their Application,A. C. Davison ; D. V. Hinkley,Cambridge University Press,9780521573917,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=1218089&pq-origsite=summon
- An Introduction to the Bootstrap,Bradley Efron; R.J. Tibshirani,Chapman and Hall/CRC,9780412042317,Available as e-book: https://www-taylorfrancis-com.bib-proxy.uhasselt.be/books/mono/10.1201/9780429246593/introduction-bootstrap-bradley-efron-tibshirani
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| 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 is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - 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. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
Simulations, Monte Carlo methods, Bootstrap techniques, Randomization methods, Permutation methods.
Aims
At the end of this course, the student Should Be Able To :
- describe basic simulation methods
- implement basic thesis simulation methods in R
- write programming code in R to run simulations
- discuss the basic concepts to the bootstrap method
- explain the different steps of the bootstrap algorithm
- Explain the difference between parametric, semiparametric and fully nonparametric bootstrap approaches
- apply the bootstrap for hypothesis testing
- explain, and compute different bootstrap confidence intervals
- apply the bootstrap on linear models, taking into account different study designs
- explain and apply randimization and permutation methods
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Distance learning ✔
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Project ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Oral exam | 50 % |
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| Other | Questions about the content/materials of the final project |
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| Other exam | 50 % |
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| Other | Written project (in three parts). |
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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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| Recommended reading |
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- Bootstrap Methods and their Application,A. C. Davison ; D. V. Hinkley,Cambridge University Press,9780521573917,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=1218089&pq-origsite=summon
- An Introduction to the Bootstrap,Bradley Efron; R.J. Tibshirani,Chapman and Hall/CRC,9780412042317,Available as e-book: https://www-taylorfrancis-com.bib-proxy.uhasselt.be/books/mono/10.1201/9780429246593/introduction-bootstrap-bradley-efron-tibshirani
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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