Fadoua Balabdaoui: Katalogdaten im Herbstsemester 2021

Name	Frau Prof. Dr. Fadoua Balabdaoui
Adresse	Mathematik, Bühlmann ETH Zürich, HG G 24.1 Rämistrasse 101 8092 Zürich SWITZERLAND
Telefon	+41 44 632 61 84
E-Mail	fadoua.balabdaoui@stat.math.ethz.ch
URL	http://stat.ethz.ch/~fadouab/
Departement	Mathematik
Beziehung	Titularprofessorin

Nummer	Titel	ECTS	Umfang	Dozierende
401-3620-20L	Student Seminar in Statistics: Inference in Some Non-Standard Regression Problems 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 KP	2S	F. Balabdaoui
Kurzbeschreibung	Review of some non-standard regression models and the statistical properties of estimation methods in such models.
Lernziel	The main goal is the students get to discover some less known regression models which either generalize the well-known linear model (for example monotone regression) or violate some of the most fundamental assumptions (as in shuffled or unlinked regression models).
Inhalt	Linear regression is one of the most used models for prediction and hence one of the most understood in statistical literature. However, linearity might be too simplistic to capture the actual relationship between some response and given covariates. Also, there are many real data problems where linearity is plausible but the actual pairing between the observed covariates and responses is completely lost or at partially. In this seminar, we review some of the non-classical regression models and the statistical properties of the estimation methods considered by well-known statisticians and machine learners. This will encompass: 1. Monotone regression 2. Single index model 3. Unlinked regression
Literatur	In the following is the tentative material that will be read and studied by each pair of students (all the items listed below are available through the ETH electronic library or arXiv). Some of the items might change. 1. Chapter 2 from the book "Nonparametric estimation under shape constraints" by P. Groeneboom and G. Jongbloed, 2014, Cambridge University Press 2. "Nonparametric shape-restricted regression" by A. Guntuoyina and B. Sen, 2018, Statistical Science, Volume 33, 568-594 3. "Asymptotic distributions for two estimators of the single index model" by Y. Xia, 2006, Econometric Theory, Volume 22, 1112-1137 4. "Least squares estimation in the monotone single index model" by F. Balabdaoui, C. Durot and H. K. Jankowski, Journal of Bernoulli, 2019, Volume 4B, 3276-3310 5. "Least angle regression" by B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, 2004, Annals of Statsitics, Volume 32, 407-499. 6. "Sharp thresholds for high dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso)" by M. Wainwright, 2009, IEEE transactions in Information Theory, Volume 55, 1-19 7."Denoising linear models with permuted data" by A. Pananjady, M. Wainwright and T. A. Courtade and , 2017, IEEE International Symposium on Information Theory, 446-450. 8. "Linear regression with shuffled data: statistical and computation limits of permutation recovery" by A. Pananjady, M. Wainwright and T. A. Courtade , 2018, IEEE transactions in Information Theory, Volume 64, 3286-3300 9. "Linear regression without correspondence" by D. Hsu, K. Shi and X. Sun, 2017, NIPS 10. "A pseudo-likelihood approach to linear regression with partially shuffled data" by M. Slawski, G. Diao, E. Ben-David, 2019, arXiv. 11. "Uncoupled isotonic regression via minimum Wasserstein deconvolution" by P. Rigollet and J. Weed, 2019, Information and Inference, Volume 00, 1-27
Voraussetzungen / Besonderes	The students need to be comfortable with regression models, classical estimation methods (Least squares, Maximum Likelihood estimation...), rates of convergence, asymptotic normality, etc.
401-4623-00L	Time Series Analysis Findet dieses Semester nicht statt.	6 KP	3G	F. Balabdaoui
Kurzbeschreibung	The 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.
Lernziel	The 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.
Inhalt	This 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
Literatur	The main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis
Voraussetzungen / Besonderes	Basic knowledge in probability and statistics
401-5640-00L	ZüKoSt: Seminar on Applied Statistics	0 KP	1K	M. Kalisch, F. Balabdaoui, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, M. Robinson, A. Sousa Bandeira, C. Strobl, S. van de Geer
Kurzbeschreibung	Etwa 5 Vorträge zur angewandten Statistik.
Lernziel	Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten.
Inhalt	In etwa 5 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen.
Voraussetzungen / Besonderes	Dies 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.