## Fadoua Balabdaoui: Catalogue data in Autumn Semester 2023 |

Name | Prof. Dr. Fadoua Balabdaoui |

Address | Mathematik, Bühlmann ETH Zürich, HG G 24.1 Rämistrasse 101 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 61 84 |

fadoua.balabdaoui@stat.math.ethz.ch | |

URL | http://stat.ethz.ch/~fadouab/ |

Department | Mathematics |

Relationship | Adjunct Professor |

Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||
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401-3620-20L | Student Seminar in Statistics: Inference in Some Non-Standard Regression Problems Number of participants limited to 24. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science. | 4 credits | 2S | F. Balabdaoui | ||||||||||||||||||||

Abstract | Review of some non-standard regression models and the statistical properties of estimation methods in such models. | |||||||||||||||||||||||

Learning objective | 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). | |||||||||||||||||||||||

Content | 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 | |||||||||||||||||||||||

Literature | 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 | |||||||||||||||||||||||

Prerequisites / Notice | 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 credits | 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 | ||||||||||||||||||||

Abstract | About 3 talks on applied statistics. | |||||||||||||||||||||||

Learning objective | See how statistical methods are applied in practice. | |||||||||||||||||||||||

Content | There will be about 3 talks on how statistical methods are applied in practice. | |||||||||||||||||||||||

Prerequisites / Notice | This 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. | |||||||||||||||||||||||

Competencies |
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406-2604-AAL | Probability and StatisticsEnrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | 8 credits | 17R | F. Balabdaoui | ||||||||||||||||||||

Abstract | - 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 | |||||||||||||||||||||||

Learning objective | 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/ | |||||||||||||||||||||||

Content | 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". | |||||||||||||||||||||||

Lecture notes | (*) Wahrscheinlichkeitsrechnung und Statistik (*) Statistics for Mathematics Both scripts can be found at https://www.stat.math.ethz.ch/~fadouab/ | |||||||||||||||||||||||

Literature | 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) |