Lectures
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 |
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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.
Magali Champion
Contact: E-mail, WebsiteApplication of L1-spectral clustering
Description: Application of L1-spectral clustering (Champion et al. 2021) to discover groups of genes associated with the development of kidney cancerMethods: L1-spectral clustering (Champion et al. 2021) which combines spectral clustering and L1-minimization
Knowledge: basic notions of maths (spectral theory, graphs theory) and R
Data: gene expression data from TCGA (kidney cancer)
Benchmark of clustering methods
Description: Benchmark of clustering methods to discover groups of genes associated with the development of kidney cancerMethods: k-means, Markov clustering algorithm, ...
Knowledge: basic notions of clustering and R
Data: gene expression data from TCGA (kidney cancer)
Identification of genes involved in the development of ER+ breast cancer
Description: Identification of genes involved in the development of ER+ breast cancerMethods: multiple tests, PCA, lasso
Knowledge: basic notions of machine learning and R
Data: gene expression data from TCGA (breast cancer)
Benchmark of gene regulatory network inference methods
Description: Benchmark of gene regulatory network inference methodsMethods: lasso, elastic net, random forest (-> causal networks, pcalg,...)
Knowledge: basic notions of graph theory, machine learning and R
Data: gene expression data from TCGA (kidney cancer)
Multi-layer gene networks
Description: Multi-layer gene networks: how to combine biological data to create a gene network?Methods: multiplex algorithm from (Didier, 15), (Cantini, 21)
Knowledge: basic notions of machine learning and R
Data: multi-omics data from TCGA
Extension of the L1-spectral clustering algorithm
Description: Extension of the L1-spectral clustering algorithm to stochastic block modelsMethods: L1-spectral clustering, stochastic block models
Knowledge: spectral theory, graph theory
Data: no data (more theoretical)
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
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
Malte Londschien (mit Peter Bühlmann)
Contact: E-mailIntegration of Change Point Detection Algorithms with Spline-Based Smoothers for Drift Correction of Metabolomics Data
Description: Metabolomics is the study of small molecules in various tissues such as blood, urine, etc. Applications of metabolomics include monitoring of clinical trials and drug and biomarker discovery. In a typical metabolomics experiment, samples are placed in numbered wells on plates and processed by mass spectrometer well by well, plate by plate. The resulting data can thus be interpreted as a high-dimensional time series. Metabolomic measurements are prone to batch effects, instrumental drifts and abrupt jumps, which need to be removed in a pre-processing step. Change (or break) point detection considers the localization of abrupt distributional changes in time series. We propose to estimate drifts and jumps simultaneously with change point detection.Methods: Change point detection
Data: Metabolomics
Knowledge: Statistical methods, programming in R or Python.
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
Comparing sequential quantile forecasts
Description: Recently, new methods have been proposed for the sequential comparison of probability forecasts for binary outcomes (https://doi.org/10.1093/biomet/asab047, https://arxiv.org/abs/2110.00115). The goal of this project is to adapt the ideas for the comparison of quantile forecasts, and to apply the methods evaluate to Covid-19 predictions.Methods: proper scoring rules, e-values, test martingales
Knowledge: basic mathematics (knowledge of stochastic processes, martingales is an advantage but not a must), R (or Python) programming
Data: Covid-19 predictions (https://github.com/reichlab/covid19-forecast-hub/blob/master/README.md)