Peter L. Bühlmann: Catalogue data in Autumn Semester 2020 |
| Name | Prof. Dr. Peter L. Bühlmann |
| Field | Mathematik |
| Address | Seminar für Statistik (SfS) ETH Zürich, HG G 17 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 73 38 |
| Fax | +41 44 632 12 28 |
| peter.buehlmann@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~peterbu |
| Department | Mathematics |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-3622-00L | Statistical Modelling  | 8 credits | 4G | P. L. Bühlmann, M. Mächler | |
| Abstract | In regression, the dependency of a random response variable on other variables is examined. We consider the theory of linear regression with one or more covariates, high-dimensional linear models, nonlinear models and generalized linear models, robust methods, model choice and nonparametric models. Several numerical examples will illustrate the theory. | ||||
| Objective | Introduction into theory and practice of a broad and popular area of statistics, from a modern viewpoint. | ||||
| Content | In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression. Durchrechnung und Diskussion von Anwendungsbeispielen. | ||||
| Lecture notes | Lecture notes | ||||
| Prerequisites / Notice | This is the course unit with former course title "Regression". Credits cannot be recognised for both courses 401-3622-00L Statistical Modelling and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden). | ||||
| 401-3627-00L | High-Dimensional Statistics Does not take place this semester. | 4 credits | 2V | P. L. Bühlmann | |
| 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 | ||||
| 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. | ||||
| Prerequisites / Notice | Knowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics). | ||||
| 401-5620-00L | Research Seminar on Statistics | 0 credits | 1K | P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur, C. Uhler, M. Wolf | |
| Abstract | Research colloquium | ||||
| Objective | |||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, M. Robinson, C. Strobl, C. Uhler, S. van de Geer | |
| Abstract | About 5 talks on applied statistics. | ||||
| Objective | See how statistical methods are applied in practice. | ||||
| Content | There will be about 5 talks on how statistical methods are applied in practice. | ||||
| Prerequisites / Notice | This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker. | ||||
| 401-5680-00L | Foundations of Data Science Seminar | 0 credits | P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, G. Rätsch, C. Uhler, S. van de Geer, F. Yang | ||
| Abstract | Research colloquium | ||||
| Objective | |||||