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401-3627-DRL 2 Credits DR D-MATH

High-Dimensional Statistics

Lecturers & Examiners: Prof. Dr. Peter L. Bühlmann
Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to with their name, course number and student ID. Please see
VVZ CR n/a

Last Updated: 2026-02-05 16:14:50

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
DR
Frequency
Every two years

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture High-Dimensional Statistics
Planned to be offered in the Spring (!) semester 2025 again, but not in the Autumn Semester 2025.
  • Thu 08:15-10:00 (CAB G 61)
2 h weekly

Offered In