Lectures
Autumn semester 2022
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Biostatistics (Dr. Matteo Tanadini)
- Applied Statistical Regression (Dr. M. Dettling)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Prof. S. van de Geer)
- Mathematik IV: Statistik (Dr. J. Ernest)
- Smoothing and Nonparametric Regression with Examples (Dr. S. Beran-Ghosh)
- Statistical and Numerical Methods for Chemical Engineers (Dr. P. Müller)
- Statistical Modelling (Prof. P. Bühlmann)
- Statistik II (D-BIOL, D-HEST) (Dr. J. Dambon)
- Statistik II (Humanmedizin) (Dr. D. Stekhoven)
- Stochastic Simulation (Dr. F. Sigrist)
- Student Seminar in Statistics: Inference in Some Non-Standard Regression Problems (Prof. F. Balabdaoui)
- Time Series Analysis (Prof. N. Meinshausen)
- Using R for Data Analysis and Graphics (Part I) (Prof. M. Mächler)
- Using R for Data Analysis and Graphics (Part II) (Prof. M. Mächler)
Spring semester 2022
- Advanced Statistical Modelling: Mixed Models (Prof. M. Mächler)
- Applied Multivariate Statistics (Dr. F. Sigrist)
- Applied Time Series (Dr. M. Dettling)
- Computational Statistics (Prof. N. Meinshausen)
- Empirical Process Theory and Applications (Prof. S. van de Geer)
- Programming with R for Reproducible Research (Prof. M. Mächler)
- Student Seminar in Statistics: Causality (Prof. P. Bühlmann, Dr. M. Champion)
- Statistics Lab (Dr. M. Kalisch, Prof. M. Mächler, Dr. L. Meier, Prof. N. Meinshausen, Prof. em. Stahel)
- Statistik I (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik und Wahrscheinlichkeitsrechnung (Dr. L. Meier)
Autumn semester 2021
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Biostatistics (Dr. Matteo Tanadini)
- Applied Statistical Regression (Dr. M. Dettling)
- Bayesian Statistics (Dr. F. Sigrist)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Prof. S. van de Geer)
- High-Dimensional Statistics (Prof. P. Bühlmann)
- Mathematik IV: Statistik (Dr. J. Ernest)
- Smoothing and Nonparametric Regression with Examples (Dr. S. Beran-Ghosh)
- Statistical and Numerical Methods for Chemical Engineers (Dr. P. Müller)
- Statistical Modelling (Dr. C. Heinze-Deml)
- Statistik II (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik II (Humanmedizin) (Dr. D. Stekhoven)
- Student Seminar in Statistics: Inference in Some Non-Standard Regression Problems (Prof. F. Balabdaoui)
- Using R for Data Analysis and Graphics (Part I) (Prof. M. Mächler)
- Using R for Data Analysis and Graphics (Part II) (Prof. M. Mächler)
Spring semester 2021
- Applied Multivariate Statistics (Dr. F. Sigrist)
- Applied Time Series (Dr. M. Dettling)
- Causality (Dr. C. Heinze-Deml)
- Computational Statistics (Prof. M. Mächler)
- Empirical Process Theory and Applications (Prof. S. van de Geer)
- On Hypothesis Testing (Prof. F. Balabdaoui)
- Programming with R for Reproducible Research (Prof. M. Mächler)
- Student Seminar in Statistics: Statistical Network Modeling (Dr. M. Azadkia, Prof. P. Bühlmann)
- Statistics Lab (Dr. M. Kalisch, Prof. M. Maathuis, Prof. M. Mächler, Dr. L. Meier, Prof. N. Meinshausen)
- Statistik I (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik und Wahrscheinlichkeitsrechnung (Dr. L. Meier)
Autumn semester 2020
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Biostatistics (Dr. Matteo Tanadini)
- Applied Statistical Regression (Dr. M. Dettling)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Prof. S. van de Geer)
- Mathematik IV: Statistik (Dr. J. Ernest)
- Statistical Modelling (Prof. P. Bühlmann, Dr. M. Mächler)
- Smoothing and Nonparametric Regression with Examples (Dr. S. Beran-Ghosh)
- Statistical and Numerical Methods for Chemical Engineers (Dr. P. Müller)
- Statistik II (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik II (Humanmedizin) (Dr. D. Stekhoven)
- Stochastic Simulation (Dr. F. Sigrist)
- Stochastik (Prof. M. Maathuis)
- Student Seminar in Statistics: Multiple Testing for Modern Data Science (Dr. M. Löffler, Dr. A. Taeb)
- Time Series Analysis (Dr. F. Balabdaoui)
- Using R for Data Analysis and Graphics (Part I) (Dr. M. Mächler)
- Using R for Data Analysis and Graphics (Part II) (Dr. M. Mächler)
Spring semester 2020
- Advanced Statistical Modelling: Mixed Models (Dr. M. Mächler)
- Applied Multivariate Statistics (Dr. F. Sigrist)
- Applied Time Series (Dr. M. Dettling)
- Causality (Dr. C. Heinze-Deml)
- Computational Statistics (Prof. M. Maathuis)
- Empirical Process Theory with Applications (Prof. S. van de Geer)
- Programming with R for Reproducible Research (Dr. M. Mächler)
- Student Seminar in Statistics: Inference in Non-Classical Regression Models (Dr. F. Balabdaoui)
- Statistik I (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik und Wahrscheinlichkeitsrechnung (Prof. P. Bühlmann)
Autumn semester 2019
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Biostatistics (Dr. Matteo Tanadini)
- Applied Statistical Regression (Dr. M. Dettling)
- Bayesian Statistics (Dr. F. Sigrist)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Prof. S. van de Geer)
- High-Dimensional Statistics (Prof. P. Bühlmann)
- Mathematics Tools in Machine Learning (Dr. F. Balabdaoui)
- Mathematik IV: Statistik (Dr. J. Ernest)
- Smoothing and Nonparametric Regression with Examples (Dr. S. Beran-Ghosh)
- Statistical and Numerical Methods for Chemical Engineers (Dr. P. Müller)
- Statistical Modelling (Dr. C. Heinze-Deml)
- Statistik II (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik II (Humanmedizin) (Dr. D. Stekhoven)
- Student Seminar in Statistics: The Art of Statistics (Prof. M. Maathuis, Prof. P. Bühlmann, Prof. S. van de Geer)
- Using R for Data Analysis and Graphics (Part I) (Dr. M. Mächler)
- Using R for Data Analysis and Graphics (Part II) (Dr. M. Mächler)
Spring semester 2019
- Applied Multivariate Statistics (Dr. F. Sigrist)
- Applied Time Series (Dr. M. Dettling)
- Causality (Dr. C. Heinze-Deml)
- Computational Statistics (Prof. M. Maathuis)
- Empirical Process Theory with Applications in Statistics and Machine Learning (Prof. S. van de Geer)
- Mixed Models (Dr. M. Mächler)
- Multivariate Statistics (Prof. N. Meinshausen)
- Programming with R for Reproducible Research (Dr. M. Mächler)
- Student Seminar in Statistics: Adversarial and Robust Machine Learning (Prof. P. Bühlmann)
- Statistik I (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik und Wahrscheinlichkeitsrechnung (Dr. L. Meier)
Autumn semester 2018
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Statistical Regression (Dr. M. Dettling)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Prof. S. van de Geer)
- Mathematik IV: Statistik (Dr. J. Ernest)
- On Hypothesis Testing (Dr. F. Balabdaoui)
- Smoothing and Nonparametric Regression with Examples (Dr. S. Beran-Ghosh)
- Statistik II (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik II (Humanmedizin) (Dr. D. Stekhoven)
- Stochastic Simulation (Dr. F. Sigrist)
- Stochastics: Probability and Statistics (Prof. M. Maathuis)
- Student Seminar in Statistics: Statistical Learning with Sparsity (Dr. M. Mächler)
- Time Series Analysis (Prof. N. Meinshausen)
- Using R for Data Analysis and Graphics (Dr. M. Tanadini, Dr. M. Mächler)
Spring semester 2018
- Applied Multivariate Statistics (Dr. F. Sigrist)
- Applied Time Series (Dr. M. Dettling)
- Causality (Prof. N. Meinshausen)
- Computational Statistics (Prof. M. Maathuis)
- Mixed Models (Dr. M. Mächler)
- Programming with R for Reproducible Research (Dr. M. Mächler)
- Regression (Prof. P. Bühlmann)
- Student Seminar in Statistics: Nonparametric Estimation under Shape-Constraints (Dr. F. Balabdaoui)
- Statistik I (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik und Wahrscheinlichkeitsrechnung (Dr. L. Meier)
Autumn semester 2017
- Advanced Computational Statistics (Prof. N. Meinshausen)
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Statistical Regression (Dr. M. Dettling)
- Bayesian Statistics (Dr. F. Sigrist)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Prof. S. van de Geer)
- Mathematik IV: Statistik (Dr. D. Stekhoven)
- On Hypothesis Testing (Dr. F. Balabdaoui)
- Student Seminar in Statistics: Computer Age Statistical Inference (Prof. M. Maathuis)
- Smoothing and Nonparametric Regression (Dr. S. Beran-Ghosh)
- Statistical and Numerical Methods for Chemical Engineers (Dr. P. Müller)
- Statistik II (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Using R for Data Analysis and Graphics (Dr. A. Drewek, Dr. M. Mächler)
Spring semester 2017
- Applied Multivariate Statistics (Dr. F. Sigrist)
- Applied Time Series (Dr. M. Dettling)
- Causality (Prof. Marloes Maathuis)
- Computational Statistics (Dr. M. Mächler, Prof. P. Bühlmann)
- Mathematik IV: Statistik (Dr. D. Stekhoven)
- Multivariate Statistics (Prof. N. Meinshausen)
- Student Seminar in Statistics: Statistical Inference under Shape Restrictions (Dr. F. Balabdaoui)
- Statistik I (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Statistik und Wahrscheinlichkeitsrechnung (Dr. L. Meier)
Autumn semester 2016
- Applied Analysis of Variance and Experimental Design (Dr. L. Meier)
- Applied Statistical Regression (Dr. M. Dettling)
- Biostatistics (Dr. B. Sick)
- Data Analytics in Organisations and Business (Dr. I. Flückiger)
- Fundamentals of Mathematical Statistics (Dr. F. Balabdaoui)
- Smoothing and Nonparametric Regression (Dr. R. Ghosh)
- Statistik II (D-BIOL, D-HEST) (Dr. M. Kalisch)
- Stochastic Simulation (Dr. F. Sigrist)
- Stochastik (RW, D-MATL, D-MAVT) (Prof. Dr. Marloes Maathuis)
- Time Series Analysis (Prof. N. Meinshausen)
- Using R for Data Analysis and Graphics (Dr. A. Drewek, Dr. A. Papritz)
Spring semester 2016
- Applied Multivariate Statistics (Prof. M. Maathuis)
- Applied Time Series Analysis (Dr. M. Dettling)
- Estimation and Testing under Sparsity (Prof. S. van de Geer)
- Computational Statistics (Dr. M. Mächler, Prof. P. Bühlmann)
- Mathematik IV (Dr. D. Stekhoven)
- Programming with R for Reproducible Research (Dr. M. Mächler)
- Regression (Prof. N. Meinshausen)
- Seminar in Statistics: Learning Blackjack (Dr. J. Peters)
- Statistics Lab (Dr. M. Kalisch, Dr. L. Meier)
- Statistik und Wahrscheinlichkeitsrechnung (D-BAUG) (Dr. L. Meier)
- Statistik I (D-BIOL) (Dr. M. Kalisch)
Previous semesters
The websites of courses taught in previous semesters can be found here.Question hours
In German: Ferienpräsenz
Lecture | Date | Time | Room |
---|
Exam review
In German: Prüfungseinsicht
Statistik und Wahrscheinlichkeitsrechnung
Mathematik IV: Statistik
Fundamentals of Mathematical Statistics
Applied ANOVA and Experimental Design
Bachelor, master and semester thesis topics
Below you can find topics for bachelor, master or semester theses that
the supervisors at the Seminar for Statistics offer.
Please note: This site is still under construction.
Peter Bühlmann
Contact: E-mailConformal prediction for anchor regression
Description: Conformal prediction leads to finite sample correct prediction intervals when the data are i.i.d. The goal is to study these methods and extend them to heterogeneous problems when using anchor regression and related techniques for domain adaptation.Methods: Linear models, machine learning algorithms, stabilization
Knowledge: Statistical methods and modeling, programming in R or Python
Data: Mostly simulated, if interested larger ICU patient data
Literature:
https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1307116?casa_token=xEbi9SO9uJ0AAAAA%3APhTQ-jYyhH9Ow_wI1DWsepy_PiwKtZ92TFy_tHDdZOIothxphTE_EPsJPXILkcj5YYZbkajD87ytB9M
https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-1/Predictive-inference-with-the-jackknife/10.1214/20-AOS1965.full
https://proceedings.neurips.cc/paper/2019/hash/8fb21ee7a2207526da55a679f0332de2-Abstract.html
https://www.pnas.org/doi/abs/10.1073/pnas.2107794118
https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12398
Markus Kalisch
Contact: E-mailDiscrete Choice Models
Description: Discrete choice models or qualitative choice models are intended to explain choices between two or more discrete alternatives, such as buying a car or not or choosing among different occupations. In this project, you will read publications in the area, write a summary, apply and implement methods in R, perform simulation studies.Methods: Extensions to linear regression motivated by economics and social sciences
Knowledge: Linear Regression
Ordinal Response Models
Description: In many applied settings the response variable is an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. In this project, you will read publications in the area, write a summary, apply and implement methods in R, perform simulation studies.Methods: Extensions to linear regression motivated by e.g. social sciences
Knowledge: Linear Regression
Generalized Additive Models
Description: A generalized additive model (GAM) is a generalized linear model in which the response variable depends linearly on unknown smooth functions of some predictor variables. In this project, you will read publications in the area, write a summary, apply and implement methods in R, perform simulation studies.Methods: Extensions to linear regression motivated by many applied fields of research
Knowledge: Linear Regression
Model-Robustness in Linear Regression
Description: Linear Regression is a simple but surprisingly powerful tool in practical data analysis problems. In this thesis (SA/BA or MA) we have a closer look at the assumptions and optimality guarantees that come with the standard linear regression. Then, we will have a closer look at the robustness of the inference if these assumptions are violated and will research on methods which are more robust wrt. violations of the assumptions.Methods: Extensions to linear regression motivated by many applied fields of research
Knowledge: Linear Regression
Lukas Meier
Contact: E-mailRegression with Interval Censoring
Description: Read publications in the area, write a summary, apply and implement methods in R, perform simulation studies.Methods: Special regression models motivated by survival analysis
Knowledge: Linear regression
Dyadic Regression Models
Description: Dyadic regression is used to model pairwise interaction data (between people, countries etc.), some models are also known as "gravity models". Read publications in the area, write a summary, apply and implement methods in R, perform simulation studies.Methods: Regression
Knowledge: Linear regression
Nicolai Meinshausen
Contact: E-mailFairness in Machine Learning
Description: Read a few key publications in the area of fairness in Machine Learning and write a concise summary, highlighting key conceptual commonalities and differencesMethods: Linear regression and classification; tree ensembles; structural causal models
Knowledge: Regression and classification; causality
Data: some standard benchmark datasets can be used but can also be more theoretical
Invariant Risk Minimization
Description: Implement the invariant risk minimization framework of Arjovski (2019) and write a discussionMethods: Linear models; tree ensembles; deep networks; causal inference
Knowledge: Machine Learning; Causality
Data: Datasets in paper or some other simple simulation data; possibly some larger datasets
Out-of-distribution generalizations
Description: Read some recent publications on out-of-distribution generalization and write a summary of their differences, advantages and drawbacks.Methods: Linear models; tree ensembles; structural causal models
Knowledge: Regression and Classification; Causality
Data: Some small simulation studies; if of interest also larger datasets on ICU patient data
Quantile Treatment Effects
Description: Read on quantile treatment effects which characterize the possibly heterogenous causal effect and write a summary of current approachesMethods: Linear models; tree ensembles; structural causal models; instrumental variables
Knowledge: Regression and Classification; Causality
Data: Can be theoretical; can also use some large-scale climate data
Christoph Schultheiss (with Peter Bühlmann)
Contact: E-mailGoodness-of-fit test for detecting local causal structures
Description: The idea would be to evolve a goodness-of-fit method that aims to find out whether fitted regression models might be causal. We came up with a method for linear models, which can be shown to do asymptotically the right thing. This could be read up here. We would like to have a similar method for a broader class of regression models. In the "population case", where one knows the exact data distribution, this is rather straight forward. How to best implement this in practice with finite data where regression functions must be estimated, and afterwards statistical tests are needed is an open question. We have some ideas that could be tried in simulations, but new ideas are welcome as well. The project work would be mainly statistical methodology and simulations.Methods: TBD
Data: Mostly simulated
Knowledge: Statistical methods, programming in R or Python.
Alexander Henzi (with Peter Bühlmann)
Contact: E-mailSmooth isotonic distributional regression
Description: Isotonic distributional regression (IDR; https://doi.org/10.1111/rssb.12450, doi.org/10.1214/19-EJS1659) is a method for estimating the conditional distribution of an outcome given covariates under monotonicity constraints. The estimator produces discrete distributions, but often one would like to have an estimate of the conditional density. The goal of this project is to investigate methods for smoothing the IDR output distributions, based on a kernel density estimation approach.Methods: kernel density estimation, shape restricted regression
Knowledge: basic knowledge of kernel density estimation, nonparametric statistics, R (or Python) programming
Cyrill Scheidegger (with Peter Bühlmann)
Contact: E-mailDeconfounding for regression trees
Description: The goal of this project would be to apply the idea of spectral deconfounding (originally introduced in the linear case, arXiv:1811.05352) to fitting regression trees. In a first part, the student would read about spectral deconfounding in the linear case. In a second part, the student would develop, implement and evaluate an algorithm to fit regression trees on confounded data. Such an algorithm might be quite slow, so a part of the project would also be to think about ways of optimizing the algorithm. Furthermore, one could also try to extend the methodology to more general tree-based methods like random forests.Methods: Spectral deconfounding, regression trees
Data: Mostly simulated
Knowledge: Linear algebra (in particular eigenvalue and singular value decomposition), programming in R or Python