Marloes H. Maathuis: Catalogue data in Spring Semester 2018

Name Prof. Dr. Marloes H. Maathuis
FieldStatistics
URLhttp://stat.ethz.ch/~maathuis
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
401-3620-18LStudent Seminar in Statistics: Nonparametric Estimation under Shape-Constraints Restricted registration - show details
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 credits2SF. Balabdaoui, P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer
AbstractStatistical 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.
Learning objectiveThe 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.
Prerequisites / NoticeWe 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.
401-3632-00LComputational Statistics10 credits3V + 2UM. H. Maathuis
AbstractComputational 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.
Learning objectiveIn 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.
ContentSee the class website
Prerequisites / NoticeAt least one semester of (basic) probability and statistics.

Programming experience is helpful but not required.
401-4620-00LStatistics Lab Restricted registration - show details
Number of participants limited to 27.
6 credits2SM. Kalisch, M. H. Maathuis, M. Mächler, 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.
ContentStudents 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 notesn/a
LiteratureThe required literature will depend on the specific statistical problem
under investigation. Some introductory material can be found below.
Prerequisites / NoticePrerequisites:
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.
401-5620-00LResearch Seminar on Statistics Information 0 credits2KP. L. Bühlmann, L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, M. Wolf
AbstractResearch colloquium
Learning objective
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl
Abstract5 to 6 talks on applied statistics.
Learning objectiveKennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
ContentIn 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 notesBei 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 / NoticeDies 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.