Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Statistik Master Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.
Kernfächer In der Regel werden die Kernfächer in jedem Themenbereich sowohl in einer mathematisch ausgerichteten als auch in einer anwendungsorientierten Art angeboten. Pro Themenbereich wird jeweils nur eine dieser beiden Arten für das Master-Diplom angerechnet.
Regression
401-3622-00L	Regression	W	8 KP	4G	P. L. Bühlmann
Kurzbeschreibung	In der Regression wird die Abhängigkeit einer zufälligen Response-Variablen von anderen Variablen untersucht. Wir betrachten die Theorie der linearen Regression mit einer oder mehreren Ko-Variablen, hoch-dimensionale lineare Modelle, nicht-lineare Modelle und verallgemeinerte lineare Modelle, Robuste Methoden, Modellwahl und nicht-parametrische Modelle.
Lernziel	Einführung in Theorie und Praxis eines umfassenden und vielbenutzten Teilgebiets der Statistik, unter Berücksichtigung neuerer Entwicklungen.
Inhalt	In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression. Durchrechnung und Diskussion von Anwendungsbeispielen.
Skript	Vorlesungsskript
Voraussetzungen / Besonderes	Credits cannot be recognised for both courses 401-3622-00L Regression and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden).

Varianzanalyse und Versuchsplanung Kein Angebot in diesem Semester.
Multivariate Statistik
Nummer	Titel	Typ	ECTS	Umfang	Dozierende
401-6102-00L	Multivariate Statistics Findet dieses Semester nicht statt.	W	4 KP	2G	keine Angaben
Kurzbeschreibung	Multivariate Statistics deals with joint distributions of several random variables. This course introduces the basic concepts and provides an overview over classical and modern methods of multivariate statistics. We will consider the theory behind the methods as well as their applications.
Lernziel	After the course, you should be able to: - describe the various methods and the concepts and theory behind them - identify adequate methods for a given statistical problem - use the statistical software "R" to efficiently apply these methods - interpret the output of these methods
Inhalt	Visualization / Principal component analysis / Multidimensional scaling / The multivariate Normal distribution / Factor analysis / Supervised learning / Cluster analysis
Skript	None
Literatur	The course will be based on class notes and books that are available electronically via the ETH library.
Voraussetzungen / Besonderes	Target audience: This course is the more theoretical version of "Applied Multivariate Statistics" (401-0102-00L) and is targeted at students with a math background. Prerequisite: A basic course in probability and statistics. Note: The courses 401-0102-00L and 401-6102-00L are mutually exclusive. You may register for at most one of these two course units.
401-0102-00L	Applied Multivariate Statistics	W	5 KP	2V + 1U	F. Sigrist
Kurzbeschreibung	Multivariate statistics analyzes data on several random variables simultaneously. This course introduces the basic concepts and provides an overview of classical and modern methods of multivariate statistics including visualization, dimension reduction, supervised and unsupervised learning for multivariate data. An emphasis is on applications and solving problems with the statistical software R.
Lernziel	After the course, you are able to: - describe the various methods and the concepts behind them - identify adequate methods for a given statistical problem - use the statistical software R to efficiently apply these methods - interpret the output of these methods
Inhalt	Visualization, multivariate outliers, the multivariate normal distribution, dimension reduction, principal component analysis, multidimensional scaling, factor analysis, cluster analysis, classification, multivariate tests and multiple testing
Skript	None
Literatur	1) "An Introduction to Applied Multivariate Analysis with R" (2011) by Everitt and Hothorn 2) "An Introduction to Statistical Learning: With Applications in R" (2013) by Gareth, Witten, Hastie and Tibshirani Electronic versions (pdf) of both books can be downloaded for free from the ETH library.
Voraussetzungen / Besonderes	This course is targeted at students with a non-math background. Requirements: ========== 1) Introductory course in statistics (min: t-test, regression; ideal: conditional probability, multiple regression) 2) Good understanding of R (if you don't know R, it is recommended that you study chapters 1,2,3,4, and 5 of "Introductory Statistics with R" from Peter Dalgaard, which is freely available online from the ETH library) An alternative course with more emphasis on theory is 401-6102-00L "Multivariate Statistics" (only every second year). 401-0102-00L and 401-6102-00L are mutually exclusive. You can register for only one of these two courses.

Zeitreihen und stochastische Prozesse
Nummer	Titel	Typ	ECTS	Umfang	Dozierende
401-6624-11L	Applied Time Series	W	5 KP	2V + 1U	M. Dettling
Kurzbeschreibung	The course starts with an introduction to time series analysis (examples, goal, mathematical notation). In the following, descriptive techniques, modeling and prediction as well as advanced topics will be covered.
Lernziel	Getting to know the mathematical properties of time series, as well as the requirements, descriptive techniques, models, advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
Inhalt	The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, state space models and spectral analysis.
Skript	A script will be available.
Voraussetzungen / Besonderes	The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, state space models and spectral analysis.

Mathematische Statistik Kein Angebot in diesem Semester.

Seite 1 von 1