Suchergebnis: Katalogdaten im Frühjahrssemester 2021
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. | ||||||
Seminar oder Semesterarbeit | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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401-3620-21L | Student Seminar in Statistics: Statistical Network Modeling Maximale Teilnehmerzahl: 48 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. | W | 4 KP | 2S | P. L. Bühlmann, M. Azadkia | |
Kurzbeschreibung | Network models can be used to analyze non-iid data because their structure incorporates interconnectedness between the individuals. We introduce networks, describe them mathematically, and consider applications. | |||||
Lernziel | Network models can be used to analyze non-iid data because their structure incorporates interconnectedness between the individuals. The participants of the seminar acquire knowledge to formulate and analyze network models and to apply them in examples. | |||||
Literatur | E. D. Kolaczyk and G. Csárdi. Statistical analysis of network data with R. Springer, Cham, Switzerland, second edition, 2020. Tianxi Li, Elizaveta Levina, and Ji Zhu. Network cross-validation by edge sampling, 2020. Preprint arXiv:1612.04717. Tianxi Li, Elizaveta Levina, and Ji Zhu. Community models for partially observed networks from surveys, 2020. Preprint arXiv:2008.03652. Tianxi Li, Elizaveta Levina, and Ji Zhu. Prediction Models for Network-Linked Data, 2018. Preprint arXiv:1602.01192. | |||||
Voraussetzungen / Besonderes | Every class will consist of an oral presentation highlighting key ideas of selected book chapters by a pair of students. Another two students will be responsible for asking questions during the presentation and providing a discussion of the the presented concepts and ideas, including pros+cons, at the end. Finally, an additional two students are responsible for giving an evaluation on the quality of the presentations/discussions and provide constructive feedback for improvement. | |||||
401-3620-20L | Student Seminar in Statistics: Inference in Non-Classical Regression Models Findet dieses Semester nicht statt. 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. | W | 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 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 4. Partially unlinked regression | |||||
Skript | No script is necessary for this seminar | |||||
Literatur | In the following is the material that will read and studied by each pair of students (all the items listed below are available through the ETH electronic library or arXiv): 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 | |||||
401-4620-00L | Statistics Lab Number of participants limited to 27. | W | 6 KP | 2S | M. Kalisch, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen | |
Kurzbeschreibung | "Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It provides a learning environment in a realistic setting. Students lead a regular consulting session at the Seminar für Statistik (SfS). After the session, the statistical data analysis is carried out and a written report and results are presented to the client. The project is also presented in the course's seminar. | |||||
Lernziel | - gain initial experience in the consultancy process - carry out a consultancy session and produce a report - apply theoretical knowledge to an applied problem After the course, students will have practical knowledge about statistical consulting. They will have determined the scientific problem and its context, enquired the design of the experiment or data collection, and selected the appropriate methods to tackle the problem. They will have deepened their statistical knowledge, and applied their theoretical knowledge to the problem. They will have gathered experience in explaining the relevant mathematical and software issues to a client. They will have performed a statistical analysis using R (or SPSS). They improve their skills in writing a report and presenting statistical issues in a talk. | |||||
Inhalt | Students participate in consulting meetings at the SfS. Several consulting dates are available for student participation. These are arranged individually. -During the first meeting the student mainly observes and participates in the discussion. During the second meeting (with a different client), the student leads the meeting. The member of the consulting team is overseeing (and contributing to) the meeting. -After the meeting, the student performs the recommended analysis, produces a report and presents the results to the client. -Finally, the student presents the case in the weekly course seminar in a talk. All students are required to attend the seminar regularly. | |||||
Skript | n/a | |||||
Literatur | The required literature will depend on the specific statistical problem under investigation. Some introductory material can be found below. | |||||
Voraussetzungen / Besonderes | Prerequisites: Sound knowledge in basic statistical methods, especially regression and, if possible, analysis of variance. Basic experience in Data Analysis with R. | |||||
401-3630-04L | Semesterarbeit Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics Weitere Angaben unter Link | W | 4 KP | 6A | Betreuer/innen | |
Kurzbeschreibung | Semesterarbeiten dienen dazu, eine statistische Fragestellung mit den entsprechenden Methoden vertieft zu studieren oder ein Fallbeispiel einer statistischen Auswertung zu erarbeiten und klar darzustellen. | |||||
Lernziel | ||||||
401-3630-06L | Semesterarbeit Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics Weitere Angaben unter Link | W | 6 KP | 9A | Betreuer/innen | |
Kurzbeschreibung | Semesterarbeiten dienen dazu, eine statistische Fragestellung mit den entsprechenden Methoden vertieft zu studieren oder ein Fallbeispiel einer statistischen Auswertung zu erarbeiten und klar darzustellen. | |||||
Lernziel | ||||||
363-1100-00L | Risk Case Study Challenge Findet dieses Semester nicht statt. | W | 3 KP | 2S | ||
Kurzbeschreibung | This seminar provides master students at ETH with the challenging opportunity to work on a real risk-modelling and risk-management case in close collaboration with a Risk Center Corporate Partner. The Corporate Partner for the Spring 2021 Edition will be announced soon. | |||||
Lernziel | During the challenge students acquire a basic understanding of o The insurance and reinsurance business o Risk management and risk modelling o The role of operational risk management as well as learn to frame a real risk-related business case together with a case manager from the Corporate Partner. Students learn to coordinate as a group. They also learn to integrate and learn from business insights in order to elaborate a solution for their case. Finally, students communicate their solution to an assembly of professionals from the Corporate Partner. | |||||
Inhalt | Students work on a real-world, risk-related case. The case is based on a business-relevant topic. Topics are provided by experts from the Risk Center's Corporate Partners. While gaining substantial insights into the industry's risk modeling and management, students explore the case or problem on their own. They work in teams and develop solutions. The cases allow students to use logical problem-solving skills with an emphasis on evidence and application. Cases offer students the opportunity to apply their scientific knowledge. Typically, the risk-related cases can be complex, contain ambiguities, and may be addressed in more than one way. During the seminar, students visit the Corporate Partner’s headquarters, conduct interviews with members of the management team as well as internal and external experts, and finally present their results in a professional manner. | |||||
Voraussetzungen / Besonderes | Please apply for this course via the official website (Link). Apply no later than February 20, 2021. The number of participants is limited to 16. |
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