Marloes H. Maathuis: Catalogue data in Spring Semester 2017

Name	Prof. Dr. Marloes H. Maathuis
Field	Statistics
URL	http://stat.ethz.ch/~maathuis
Department	Mathematics
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
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.
401-4620-00L	Statistics Lab Number of participants limited to 27.	6 credits	2S	M. Kalisch, M. H. Maathuis, L. Meier, N. Meinshausen
Abstract	"Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It provides a learning environment in a realistic setting. Students lead a regular consulting session at the Seminar für Statistik (SfS). After the session, the statistical data analysis is carried out and a written report and results are presented to the client. The project is also presented in the course's seminar.
Learning objective	- gain initial experience in the consultancy process - carry out a consultancy session and produce a report - apply theoretical knowledge to an applied problem After the course, students will have practical knowledge about statistical consulting. They will have determined the scientific problem and its context, enquired the design of the experiment or data collection, and selected the appropriate methods to tackle the problem. They will have deepened their statistical knowledge, and applied their theoretical knowledge to the problem. They will have gathered experience in explaining the relevant mathematical and software issues to a client. They will have performed a statistical analysis using R (or SPSS). They improve their skills in writing a report and presenting statistical issues in a talk.
Content	Students participate in consulting meetings at the SfS. Several consulting dates are available for student participation. These are arranged individually. -During the first meeting the student mainly observes and participates in the discussion. During the second meeting (with a different client), the student leads the meeting. The member of the consulting team is overseeing (and contributing to) the meeting. -After the meeting, the student performs the recommended analysis, produces a report and presents the results to the client. -Finally, the student presents the case in the weekly course seminar in a talk. All students are required to attend the seminar regularly.
Lecture notes	n/a
Literature	The required literature will depend on the specific statistical problem under investigation. Some introductory material can be found below.
Prerequisites / Notice	Prerequisites: Sound knowledge in basic statistical methods, especially regression and, if possible, analysis of variance. Basic experience in Data Analysis with R and/or SPSS. Useful background lectures and material: -Applied Statistical Regression (Dr. Marcel Dettling) http://stat.ethz.ch/education/semesters/as2010/semesters/as2010/asr -Angewandte statistische Regression, mit Ergänzung (Prof. Werner Stahel, Dr. Markus Kalisch) Script: http://stat.ethz.ch/~stahel/courses/regression/ -Applied Analysis of Variance and Experimental Design (Prof. M Müller) http://stat.ethz.ch/education/semesters/as2010/anova -W. Stahel, Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, (5. Auflage), Vieweg, 2005. Useful material on Statistical Software (R and/or SPSS): -401-6215-00L Using R for Statistical Data Analysis and Graphics (Dr. M. Mächler, Dr. A. J. Papritz, Dr. C. B. Schwierz). An older version of this course can be found on: http://stat.ethz.ch/ stahel/courses/R/ -An Introduction to R. http://stat.ethz.ch/CRAN/doc/manuals/R-intro.pdf -SPSS Course and Exercises: ftp://stat.ethz.ch/U/sfs/SPSSKurs/ -Andy Field, Discovering Statistics Using SPSS, 3rd Edition, 2009, SAGE.
401-4632-15L	Causality	4 credits	2G	M. H. Maathuis
Abstract	In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
Learning objective	After this course, you should be able to - understand the language and concepts of causal inference - know the assumptions under which one can infer causal relations from observational and/or interventional data - describe and apply different methods for causal structure learning - given data and a causal structure, derive causal effects and predictions of interventional experiments
Prerequisites / Notice	Prerequisites: basic knowledge of probability theory and regression
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.