Marloes H. Maathuis: Katalogdaten im Frühjahrssemester 2018

Name	Frau Prof. Dr. Marloes H. Maathuis
Lehrgebiet	Statistik
URL	http://stat.ethz.ch/~maathuis
Departement	Mathematik
Beziehung	Ordentliche Professorin

Nummer	Titel	ECTS	Umfang	Dozierende
401-3620-18L	Student Seminar in Statistics: Nonparametric Estimation under Shape-Constraints Maximale Teilnehmerzahl: 22 Hauptsächlich für Studierende der Bachelor- und Master-Studiengänge Mathematik, welche nach der einführenden Lerneinheit 401-2604-00L Wahrscheinlichkeit und Statistik (Probability and Statistics) mindestens ein Kernfach oder Wahlfach in Statistik besucht haben.	4 KP	2S	F. Balabdaoui, P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer
Kurzbeschreibung	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, ROC curve...). Under shape restrictions, we mean a variety of constraints. Examples thereof include monotonicity, bounded variation, convexity, k-monotonicity or log-concavity.
Lernziel	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 monotone estimator of a regression curve or a probability density. During the Seminar, the students will have to study some selected chapters from the books "Statistical Inference under Order Restrictions" by Barlow, Bartholomew, Bremner and Brunk, and "Nonparametric estimation under shape constraints" by Groeneboom and Jongbloed. Some "famous" articles on the subject will be also studied.
Voraussetzungen / Besonderes	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.
401-3632-00L	Computational Statistics	10 KP	3V + 2U	M. H. Maathuis
Kurzbeschreibung	Computational Statistics deals with modern statistical methods of data analysis (aka "data science") for prediction and inference. The course provides an overview of existing methods. The course is hands-on, and methods are applied using the statistical programming language R.
Lernziel	In this class, the student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R.
Voraussetzungen / Besonderes	At least one semester of (basic) probability and statistics. Programming experience is helpful but not required.
401-4620-00L	Statistics Lab Maximale Teilnehmerzahl: 27	6 KP	2S	M. Kalisch, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen
Kurzbeschreibung	"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.
Lernziel	- 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.
Inhalt	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.
Skript	n/a
Literatur	The required literature will depend on the specific statistical problem under investigation. Some introductory material can be found below.
Voraussetzungen / Besonderes	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.
401-5620-00L	Research Seminar on Statistics	0 KP	2K	P. L. Bühlmann, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, M. Wolf
Kurzbeschreibung	Forschungskolloquium
Lernziel
401-5640-00L	ZüKoSt: Seminar on Applied Statistics	0 KP	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
Kurzbeschreibung	5 bis 6 Vorträge zur angewandten Statistik.
Lernziel	Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
Inhalt	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.
Skript	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.
Voraussetzungen / Besonderes	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.