Nicolai Meinshausen: Catalogue data in Autumn Semester 2023 |
| Name | Prof. Dr. Nicolai Meinshausen |
| Field | Statistics |
| Address | Professur für Statistik ETH Zürich, HG G 23.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 32 74 |
| meinshausen@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~nicolai |
| Department | Mathematics |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 401-0624-00L | Mathematics IV: Statistics | 4 credits | 2V + 1U | N. Meinshausen | ||||||||||||||||||||
| Abstract | Introduction to basic methods and fundamental concepts of statistics and probability theory for practicioners in natural sciences. The concepts will be illustrated with some real data examples and applied using the statistical software R. | |||||||||||||||||||||||
| Learning objective | Capacity to learn from data; good practice when dealing with data and recognizing possible fraud in statistics; basic knowledge about the laws of randomness and stochastic thinking (thinking in probabilities); application of simple methods in inferential statistics (e.g., several hypothesis tests will be introduced), i.a. also using the statistical software R. The lecture will be held in German. | |||||||||||||||||||||||
| Content | Einführung in die Wahrscheinlichkeitsrechnung (Grundregeln, Zufallsvariablen, diskrete und stetige Verteilungen, Ausblick auf Grenzwertsätze). Beschreibende Statistik (einschliesslich grafische Methoden). Methoden der Analytischen Statistik: Schätzungen, Tests (einschliesslich Binomialtest, t-Test, Vorzeichentest, F-Test, Wilcoxon-Test), Vertrauensintervalle, Vorhersageintervalle, Korrelation, einfache und multiple lineare Regression. Einführung in die statistische Programmiersprache R. | |||||||||||||||||||||||
| Lecture notes | Ausführliches Skript zur Vorlesung ist erhältlich. | |||||||||||||||||||||||
| Literature | Stahel, W.: Statistische Datenanalyse. Vieweg, 5. Auflage 2008 (als ergänzende Lektüre) | |||||||||||||||||||||||
| Prerequisites / Notice | Die Übungen (ca. die Hälfte der Kontaktstunden; einschliesslich Computerübungen) sind ein wichtiger Bestandteil der Lehrveranstaltung. Voraussetzungen: Mathematik I, II | |||||||||||||||||||||||
| 401-4623-DRL | Time Series Analysis  Does not take place this semester. 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 info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 | 2 credits | 2G | N. Meinshausen | ||||||||||||||||||||
| Abstract | The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ARMA processes, Spectral Analysis, Forecasting, Nonstationary Models, ARIMA Models and an introduction to GARCH models. | |||||||||||||||||||||||
| Learning objective | The goal of the course is to have a a good overview of the different types of time series and the approaches used in their statistical analysis. | |||||||||||||||||||||||
| Content | This course treats modeling and analysis of time series, that is random variables which change in time. As opposed to the i.i.d. framework, the main feature exibited by time series is the dependence between successive observations. The key topics which will be covered as: Stationarity Autocorrelation Trend estimation Elimination of seasonality Spectral analysis, spectral densities Forecasting ARMA, ARIMA, Introduction into GARCH models | |||||||||||||||||||||||
| Literature | The main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis | |||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge in probability and statistics | |||||||||||||||||||||||
| 401-4623-00L | Time Series Analysis Does not take place this semester. | 4 credits | 2G | N. Meinshausen | ||||||||||||||||||||
| Abstract | The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ARMA processes, Spectral Analysis, Forecasting, Nonstationary Models, ARIMA Models and an introduction to GARCH models. | |||||||||||||||||||||||
| Learning objective | The goal of the course is to have a a good overview of the different types of time series and the approaches used in their statistical analysis. | |||||||||||||||||||||||
| Content | This course treats modeling and analysis of time series, that is random variables which change in time. As opposed to the i.i.d. framework, the main feature exibited by time series is the dependence between successive observations. The key topics which will be covered as: Stationarity Autocorrelation Trend estimation Elimination of seasonality Spectral analysis, spectral densities Forecasting ARMA, ARIMA, Introduction into GARCH models | |||||||||||||||||||||||
| Literature | The main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis | |||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge in probability and statistics | |||||||||||||||||||||||
| 401-5620-00L | Research Seminar on Statistics  | 0 credits | 1K | P. L. Bühlmann, N. Meinshausen, J. Peters, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur, M. Wolf | ||||||||||||||||||||
| Abstract | Research colloquium | |||||||||||||||||||||||
| Learning objective | ||||||||||||||||||||||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl | ||||||||||||||||||||
| Abstract | About 3 talks on applied statistics. | |||||||||||||||||||||||
| Learning objective | See how statistical methods are applied in practice. | |||||||||||||||||||||||
| Content | There will be about 3 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. | |||||||||||||||||||||||
| Competencies |
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