401-3627-00L  High-Dimensional Statistics

SemesterAutumn Semester 2019
LecturersP. L. Bühlmann
Periodicitytwo-yearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-3627-00 VHigh-Dimensional Statistics2 hrs
Thu08:15-10:00HG D 7.1 »
P. L. Bühlmann

Catalogue data

Abstract"High-Dimensional Statistics" deals with modern methods and theory for statistical inference when the number of unknown parameters is of much larger order than sample size. Statistical estimation and algorithms for complex models and aspects of multiple testing will be discussed.
Learning objectiveKnowledge of methods and basic theory for high-dimensional statistical inference
ContentLasso and Group Lasso for high-dimensional linear and generalized linear models; Additive models and many smooth univariate functions; Non-convex loss functions and l1-regularization; Stability selection, multiple testing and construction of p-values; Undirected graphical modeling
LiteraturePeter Bühlmann and Sara van de Geer (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Verlag.
ISBN 978-3-642-20191-2.
Prerequisites / NoticeKnowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics).

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersP. L. Bühlmann
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 60 minutes
Additional information on mode of examinationStudents must take the exam in Winter 2020 or in Summer 2020. Be aware that no exam will be offered afterwards until the course will be read again.
Written aids2 pages handwritten notes
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

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Only public learning materials are listed.

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Offered in

ProgrammeSectionType
Data Science MasterCore ElectivesWInformation
Doctoral Department of MathematicsGraduate SchoolWInformation
Mathematics BachelorSelection: Probability Theory, StatisticsWInformation
Mathematics MasterSelection: Probability Theory, StatisticsWInformation
Computational Science and Engineering BachelorElectivesWInformation
Computational Science and Engineering MasterElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation