Nicolai Meinshausen: Catalogue data in Autumn Semester 2023

Name Prof. Dr. Nicolai Meinshausen
FieldStatistics
Address
Professur für Statistik
ETH Zürich, HG G 23.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 32 74
E-mailmeinshausen@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~nicolai
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
401-0624-00LMathematics IV: Statistics4 credits2V + 1UN. Meinshausen
AbstractIntroduction 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.
ObjectiveCapacity 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.
ContentEinführung 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 notesAusführliches Skript zur Vorlesung ist erhältlich.
LiteratureStahel, W.: Statistische Datenanalyse. Vieweg, 5. Auflage 2008 (als ergänzende Lektüre)
Prerequisites / NoticeDie Übungen (ca. die Hälfte der Kontaktstunden; einschliesslich Computerübungen) sind ein wichtiger Bestandteil der Lehrveranstaltung.

Voraussetzungen: Mathematik I, II
401-4623-DRLTime Series Analysis Restricted registration - show details
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 Link with their name, course number and student ID. Please see Link
2 credits2GN. Meinshausen
AbstractThe 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.
ObjectiveThe 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.
ContentThis 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
LiteratureThe main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis
Prerequisites / NoticeBasic knowledge in probability and statistics
401-4623-00LTime Series Analysis
Does not take place this semester.
4 credits2GN. Meinshausen
AbstractThe 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.
ObjectiveThe 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.
ContentThis 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
LiteratureThe main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis
Prerequisites / NoticeBasic knowledge in probability and statistics
401-5620-00LResearch Seminar on Statistics Information 0 credits1KP. L. Bühlmann, N. Meinshausen, J. Peters, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur
AbstractResearch colloquium
Objective
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. 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
AbstractAbout 3 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 3 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis 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.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Techniques and Technologiesfostered
Method-specific CompetenciesDecision-makingfostered
Problem-solvingfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered