Computational Statistics

Course goals

ALL PARTICIPANTS WILL BE PLACED ON THE WAITING LIST UNTIL TWO WEEKS BEFORE THE START OF THE COURSE

GRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)

POSTGRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)  or online (depending on your registration)

(FYI: the other options are options for Continuing Education (onderwijs voor professionals))



At the end of the course, the student:

will have developed advanced and computationally efficient R programming skills,
is able to conduct and report on simulation studies, comparing the performance of statistical methods in specific settings,
is able to implement and use methods for statistical inference such as the bootstrap and permutation test,
will be familiar with the Metropolis-Hastings algorithm, as an example of a Markov Chain Monte Carlo method,
is familiar with some widely used numerical methods,
will be able to translate new statistical methods from the literature into a usable R program.

Content

Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl

Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. NOTE Students of the MSc Epidemiology (Post Graduate) that register in time (i.e. at least two weeks before the start of a course) will always be admitted to the course unless it is completely full. Other students will receive information about their application two weeks before the start of the course.

Course coordinator:
Dr. R.K. (Rebecca) Stellato, UMC Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands

Course description:
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.

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. 1) evaluating and comparing the performance of different statistical techniques in a specific setting using simulation, 2) implementing complex methods such as an EM algorithm to fit a joint model, 3) implementing the bootstrap to obtain a standard error estimate which is not available in closed-form. We will also develop advanced R programming skills

Literature/study material used:
Extensive use will be made of the book Statistical Computing with R, by Maria L. Rizzo, Chapman & Hall/CRC, ISBN: 9781584885450. Please read the first chapter before start of the course!
  
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
 
Optional for students in other GSLS Master’s programme:
Yes
 
Prerequisite knowledge:
Introduction to Statistics
Classical Methods in Data Analysis
Modern Methods in Data Analysis