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Nicolai Meinshausen: Katalogdaten im Herbstsemester 2018

NameHerr Prof. Dr. Nicolai Meinshausen
LehrgebietStatistik
Adresse
Professur für Statistik
ETH Zürich, HG G 24.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 32 74
E-Mailmeinshausen@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~nicolai
DepartementMathematik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
401-3620-68LStudent Seminar in Statistics: Statistical Learning with Sparsity Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 24

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. Das Seminar wird auch für Studierende der Master-Studiengänge Statistik bzw. Data Science angeboten.
4 KP2SM. Mächler, M. H. Maathuis, N. Meinshausen, S. van de Geer
KurzbeschreibungWe study selected chapters from the 2015 book "Statistical Learning with Sparsity" by Trevor Hastie, Rob Tibshirani and Martin Wainwright.

(details see below)
LernzielDuring this seminar, we will study roughly one chapter per week from the book. You will obtain a good overview of the field of sparse & high-dimensional modeling of modern statistics.
Moreover, you will practice your self-studying and presentation skills.
Inhalt(From the book's preface:) "... summarize the actively developing
field of statistical learning with sparsity.
A sparse statistical model is one having only a small number of nonzero parameters or weights. It represents a classic case of “less is more”: a sparse model can be much easier to estimate and interpret than a dense model.
In this age of big data, the number of features measured on a person or object can be large, and might be larger than the number of observations. The sparsity assumption allows us to tackle such problems and extract useful and reproducible patterns from big datasets."

For presentation of the material, occasionally you'd consider additional published research, possibly e.g., for "High-Dimensional Inference"
SkriptWebsite: with groups, FAQ, topics, slides, and Rscripts :
https://stat.ethz.ch/lectures/as18/seminar.php#course_materials
LiteraturTrevor Hastie, Robert Tibshirani, Martin Wainwright (2015)
Statistical Learning with Sparsity: The Lasso and Generalization
Monographs on Statistics and Applied Probability 143
Chapman Hall/CRC
ISBN 9781498712170

Access :

- https://www.taylorfrancis.com/books/9781498712170
(full access via ETH (library) network, if inside ETH (VPN))

- Author's website (includes errata, updated pdf, data):
https://web.stanford.edu/~hastie/StatLearnSparsity/
Voraussetzungen / BesonderesWe require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics, as well as some experience with the statistical software R.

Topics will be assigned during the first meeting.
401-4619-67LAdvanced Topics in Computational Statistics
Findet dieses Semester nicht statt.
4 KP2VN. Meinshausen
KurzbeschreibungThis lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.
LernzielStudents learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
InhaltThe main focus will be on graphical models in various forms:
Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models
Voraussetzungen / BesonderesWe assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.
401-4623-00LTime Series Analysis6 KP3GN. Meinshausen
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
401-5620-00LResearch Seminar on Statistics Information 0 KP2KL. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf
KurzbeschreibungResearch colloquium
Lernziel
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 KP1KM. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer
KurzbeschreibungEtwa 5 Vorträge zur angewandten Statistik.
LernzielKennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten.
InhaltIn etwa 5 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen.
Voraussetzungen / BesonderesDies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben.
Nach besonderem Programm:
http://stat.ethz.ch/events/zukost
Lehrsprache ist Englisch oder Deutsch je nach ReferentIn.