Sara van de Geer: Catalogue data in Spring Semester 2017 |
| Name | Prof. em. Dr. Sara van de Geer |
| Field | Mathematic |
| Address | Seminar für Statistik (SfS) ETH Zürich, HG GO 14.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 22 52 |
| sara.vandegeer@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~vsara |
| Department | Mathematics |
| Relationship | Professor emerita |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-2604-00L | Probability and Statistics | 7 credits | 4V + 2U | S. van de Geer | |
| Abstract | - Laplace models, random walks, conditional probabilities, independence. - Kolmogorov's axioms, random variables, moments, multivariate distributions, laws of large numbers and central limit theorem. - Point estimators, tests and confidence intervals. | ||||
| Learning objective | The goal of this course is to provide an introduction to the basic ideas and concepts from probability theory and mathematical statistics. This includes a mathematically rigorous treatment as well as intuition and getting acquainted with the ideas behind the definitions. The course does not use measure theory systematically, but it does point out where this is required and what the connections are. | ||||
| Content | - Diskrete Wahrscheinlichkeitsräume: Laplace-Modelle, Binomial- und Poissonverteilung, bedingte Wahrscheinlichkeiten, Unabhängigkeit, Irrfahrten, erzeugende Funktionen, eventuell Markovketten. - Allgemeine Wahrscheinlichkeitsräume: Axiome von Kolmogorov, Zufallsvariablen und ihre Verteilungen, Erwartungswert und andere Kennzahlen, Entropie, charakteristische Funktionen, mehrdimensionale Verteilung inkl. Normalverteilung, Summen von Zufallsvariablen. - Grenzwertsätze: Schwaches und starkes Gesetz der grossen Zahlen, zentraler Grenzwertsatz. - Statistik: Fragestellungen der Statistik (Schätzen, Vertrauensintervalle, Testen), Verknüpfung Statistik und Wahrscheinlichkeit, Neyman-Pearson Lemma, Wilcoxon-, t- und Chiquadrat-Test, Beurteilung von Schätzern, kleinste Quadrate. | ||||
| Literature | "Fundamentals of Probability: a First Course" by Anirban DasGupta Springer 2010 | ||||
| 401-3620-17L | Student Seminar in Statistics: Statistical Inference under Shape Restrictions  Number of participants limited to 22. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. | 4 credits | 2S | F. Balabdaoui, P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer | |
| Abstract | Statistical inference based on a random sample can be performed under additional shape restrictions on the unknown entity to be estimated (regression curve, probability density,...). Under shape restrictions, we mean a variety of constraints. Examples thereof include monotonicity, bounded variation, convexity, k-monotonicity or log-concavit. | ||||
| Learning objective | The main goal of this Student Seminar is to get acquainted with the existing approaches in shape constrained estimation. The students will get to learn that specific estimation techniques can be used under shape restrictions to obtain better estimators, especially for small/moderate sample sizes. Students will also have the opportunity to learn that one of the main merits of shape constrained inference is to avoid choosing some arbitrary tuning parameter as it is the case with bandwidth selection in kernel estimation methods. Furthemore, students will get to read about some efficient algorithms that can be used to fastly compute the obtained estimators. One of the famous algoritms is the so-called PAVA (Pool Adjacent Violators Algorithm) used under monotonicity to compute a regression curve or a probability density. During the Seminar, the students will have to study some selected chapters from the book "Statistical Inference under Order Restrictions" by Barlow, Bartholomew, Bremner and Brunk as well as some "famous" articles on the subject. | ||||
| Prerequisites / Notice | We require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics and basic knowledge in computer programming. Topics will be assigned during the first meeting. | ||||
| 401-5620-00L | Research Seminar on Statistics  | 0 credits | 2K | P. L. Bühlmann, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf | |
| Abstract | Research colloquium | ||||
| Learning objective | |||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |
| Abstract | 5 to 6 talks on applied statistics. | ||||
| Learning objective | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin. | ||||
| Content | In 5-6 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. 3 bis 4 der Vorträge stehen in der Regel unter einem Semesterthema. | ||||
| Lecture notes | Bei manchen Vorträgen werden Unterlagen verteilt. Eine Zusammenfassung ist kurz vor den Vorträgen im Internet unter http://stat.ethz.ch/talks/zukost abrufbar. Ankündigunen der Vorträge werden auf Wunsch zugesandt. | ||||
| Prerequisites / Notice | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm. Koordinator M. Kalisch, Tel. 044 632 3435 Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. Course language is English or German and may depend on the speaker. | ||||
| 406-2604-AAL | Probability and Statistics Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | 7 credits | 15R | S. van de Geer | |
| Abstract | Introduction to probability and statistics with many examples, based on chapters from the books "Probability and Random Processes" by G. Grimmett and D. Stirzaker and "Mathematical Statistics and Data Analysis" by J. Rice. | ||||
| Learning objective | The goal of this course is to provide an introduction to the basic ideas and concepts from probability theory and mathematical statistics. In addition to a mathematically rigorous treatment, also an intuitive understanding and familiarity with the ideas behind the definitions are emphasized. Measure theory is not used systematically, but it should become clear why and where measure theory is needed. | ||||
| Content | Probability: Chapters 1-5 (Probabilities and events, Discrete and continuous random variables, Generating functions) and Sections 7.1-7.5 (Convergence of random variables) from the book "Probability and Random Processes". Most of this material is also covered in Chap. 1-5 of "Mathematical Statistics and Data Analysis", on a slightly easier level. Statistics: Sections 8.1 - 8.5 (Estimation of parameters), 9.1 - 9.4 (Testing Hypotheses), 11.1 - 11.3 (Comparing two samples) from "Mathematical Statistics and Data Analysis". | ||||
| Literature | Geoffrey Grimmett and David Stirzaker, Probability and Random Processes. 3rd Edition. Oxford University Press, 2001. John A. Rice, Mathematical Statistics and Data Analysis, 3rd edition. Duxbury Press, 2006. | ||||