Computational statistics concerns the development, implementation and study of computationally intensive statistical methods. Such methods are often used e.g. in the fields of data visualization, the analysis of large datasets, Monte Carlo simulation, resampling methods such as the bootstrap, permutational methods, Markov Chain Monte Carlo methods and various numerical methods of equation solving such as the EM algorithm and Newton-Raphson iteration. A very powerful tool to implement such methods is the R statistical programming language.
- Start date(s):
- 9 March 2020
9 March 2020
- Time investment:
- Five full working days
- University Medical Center Utrecht
- Faculty of Medicine
- Fee: This fee is exempt from VAT
- € 830
- Extra information:
This course consists of lectures, computer practicals and self study. Students will preferably have completed the courses Classical Methods in Data Analysis and Modern Methods in Data Analysis or their equivalents.
This course will present essential methods in computational statistics in a practical manner, using real-world datasets and statistical problems. Examples will include e.g.
- Evaluating and comparing the performance of different statistical techniques in a specific setting using simulation.
- Implementing complex methods such as an EM algorithm to fit a joint model.
- Implementing the bootstrap to obtain a standard error estimate which is not available in closed-form. We will also develop advanced R programming skills.
At the end of the course, students are able to:
- Develop advanced and computationally efficient R programming skills,
- Conduct and report on simulation studies, comparing the performance of statistical methods in specific settings,
- Implement and use methods for statistical inference such as the bootstrap and permutation test,
- Be familiar with the Metropolis-Hastings algorithm, as an example of a Markov Chain Monte Carlo method,
- Describe widely used numerical methods,
- Translate new statistical methods from the literature into a usable R program.