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401-3627-00L 4 Credits BSC , DR , MSC D-ITET , D-MATH , D-INFK
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High-Dimensional Statistics

Lecturers & Examiners: Prof. Dr. Peter L. Bühlmann
VVZ CR n/a

Last Updated: 2026-02-05 15:48:05

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.

Objective

Knowledge of methods and basic theory for high-dimensional statistical inference

Content

Lasso 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

Resources

Literature

Peter Bühlmann and Sara van de Geer (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Verlag. ISBN 978-3-642-20191-2.

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Every two years

Examination

Type
session examination
Mode
written 60 minutes
Aids
One sheet of paper (A4, front and back) with a machine- or handwritten summary.
Students must take the exam in Winter 2021/22 or in Summer 2022. Be aware that no exam will be offered afterwards until the course will be read again.

Course Components

Type Title Time & Place Hours
lecture High-Dimensional Statistics
  • Thu 08:15-10:00 (CAB G 61)
2 h weekly

Offered In