Course ObjectiveTeaching students the adjustments to classical statistical methodology,
necessary to analyze high-dimensional data.
Course ContentThis course gives an overview of modern statistical methods that are
used in the analysis of big or high-dimensional data. Such data usually
comprise a limited number of individuals that have been characterized
with respect to many traits. These data arises in genomics, where
genetic information is measured for many thousands of genes
simultaneously, in functional MRI imaging of
the brain, but also in economic applications.
The course covers some of the most important statistical issues for big
or high-dimensional data, including: a) multiple testing, the
family-wise error rate and false discovery rate control; b) shrinkage,
Stein's estimator; c) penalized estimation, in particular ridge and
lasso regression; and (time-allowing) d) penalized estimation of
Several types of high-dimensional data will be discussed and used as
examples during the course. In terms of applications the course focuses
on cancer genomics, but theoretical aspects will apply to other fields
Teaching MethodsLectures + exercises
Method of AssessmentWritten exam
Literature"Tutorial in biostatistics: multiple hypothesis testing in genomics" by
Goeman & Solari (article in Statistics in Medicine)
"Lecture notes on ridge regression" by van Wieringen (available via
Plus additional handouts provided by the lecturer.
Target AudiencemMath, mSFM
Custom Course RegistrationThis course is offered every other year. The next opportunity to enroll will be 2019-2020.
Recommended background knowledgeThe course requires familiarity with statistics, generalized linear
models, linear algebra, probability and basic analysis.
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||dr. W.N. van Wieringen|
|Examiner||dr. W.N. van Wieringen|
dr. W.N. van Wieringen
You need to register for this course yourself
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