Fadoua Balabdaoui: Katalogdaten im Herbstsemester 2023 |
| 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 |
| fadoua.balabdaoui@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~fadouab/ |
| Departement | Mathematik |
| Beziehung | Titularprofessorin |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||
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| 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. | |||||||||||||||||||||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics  | 0 KP | 1K | M. 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 | ||||||||||||||||||||
| Kurzbeschreibung | Etwa 3 Vorträge zur angewandten Statistik. | |||||||||||||||||||||||
| Lernziel | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten. | |||||||||||||||||||||||
| Inhalt | In etwa 3 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. | |||||||||||||||||||||||
| Kompetenzen |
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| 406-2604-AAL | Probability and Statistics Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben. Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen. | 8 KP | 17R | F. Balabdaoui | ||||||||||||||||||||
| Kurzbeschreibung | - Probability spaces - Discrete models, Randiom walk - Conditional probabilities, independence - Continuous models - Limit theorems - Methods of moments - Maximum likelihood estimation - Hypothesis testing - Confidence intervals - Introductory Bayesian statistics - Linear regression model | |||||||||||||||||||||||
| Lernziel | The first part of the course gives an overview of the main concepts needed to understand probability theory (sample spaces, discrete models, random walk, contiuous models and limit theorems such as the Laws of Large Numbers and the Central limit theorem). It will be based on the German script "Wahrscheinlichkeitsrechnung und Statistik". The second part covers some fundamental results of mathematical statistics including estimation methods, hypothesis testing as well as the linear regression model. For this part, we will use the script "Statistics for Mathematics". Both scripts are available at https://www.stat.math.ethz.ch/~fadouab/ | |||||||||||||||||||||||
| Inhalt | Probability: Chapters 1-5 (Probabilities and events, Discrete and continuous random variables, Generating functions) and Sections 7.1-7.5 (Convergence of random variables) from the book "Probability and Random Processes". Most of this material is also covered in Chap. 1-5 of "Mathematical Statistics and Data Analysis", on a slightly easier level. Statistics: Sections 8.1 - 8.5 (Estimation of parameters), 9.1 - 9.4 (Testing Hypotheses), 11.1 - 11.3 (Comparing two samples) from "Mathematical Statistics and Data Analysis". | |||||||||||||||||||||||
| Skript | (*) Wahrscheinlichkeitsrechnung und Statistik (*) Statistics for Mathematics Both scripts can be found at https://www.stat.math.ethz.ch/~fadouab/ | |||||||||||||||||||||||
| Literatur | A. DasGupta, Fundamentals of Probability: A First Course, Springer (2010) R. Berger and G. Casella, Statistical Inference, Duxbury Press (1990) J. A. Rice, Mathematical Statistics and Data Analysis, Wadsworth, second edition (1995) H.-O. Georgii, Stochastik, de Gruyter, 5. Auflage (2015) A. Irle, Wahrscheinlichkeitstheorie und Statistik, Teubner (2001) | |||||||||||||||||||||||