# Search result: Catalogue data in Spring Semester 2020

Statistics Master The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible. | ||||||

Seminar or Semester Paper | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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401-4620-00L | Statistics Lab Number of participants limited to 27. | W | 6 credits | 2S | M. Kalisch, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen | |

Abstract | "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. | |||||

Objective | - 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. | |||||

Content | 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. | |||||

Lecture notes | n/a | |||||

Literature | The required literature will depend on the specific statistical problem under investigation. Some introductory material can be found below. | |||||

Prerequisites / Notice | 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 | Semester Paper Successful participation in the course unit 401-2000-00L Scientific Works in Mathematics is required. For more information, see www.math.ethz.ch/intranet/students/study-administration/theses.html | W | 4 credits | 6A | Supervisors | |

Abstract | Semester papers serve to delve into a problem in statistics and to study it with the appropriate methods or to compile and clearly exhibit a case study of a statistical evaluation. | |||||

Objective | ||||||

401-3630-94L | Semester Paper Successful participation in the course unit 401-2000-00L Scientific Works in Mathematics is required. For more information, see www.math.ethz.ch/intranet/students/study-administration/theses.html | W | 4 credits | 6A | Supervisors | |

Abstract | Semester papers serve to delve into a problem in statistics and to study it with the appropriate methods or to compile and clearly exhibit a case study of a statistical evaluation. | |||||

Objective | ||||||

401-3630-06L | Semester Paper Successful participation in the course unit 401-2000-00L Scientific Works in Mathematics is required. For more information, see www.math.ethz.ch/intranet/students/study-administration/theses.html | W | 6 credits | 9A | Supervisors | |

Abstract | Semester papers serve to delve into a problem in statistics and to study it with the appropriate methods or to compile and clearly exhibit a case study of a statistical evaluation. | |||||

Objective | ||||||

401-3620-20L | Student Seminar in Statistics: Inference in Non-Classical Regression Models 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. | W | 4 credits | 2S | F. Balabdaoui | |

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

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 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 | |||||

Lecture notes | No script is necessary for this seminar | |||||

Literature | 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 | |||||

401-3940-20L | Student Seminar in Mathematics and Data: Optimization of Random Functions Number of participants limited to 12. | W | 4 credits | 2S | A. Bandeira | |

Abstract | More information at course webpage: https://people.math.ethz.ch/~abandeira/Spring2020.StudentSeminar.html | |||||

Objective | ||||||

363-1100-00L | Risk Case Study Challenge Does not take place this semester. | W | 3 credits | 2S | A. Bommier, S. Feuerriegel | |

Abstract | This seminar provides master students at ETH with the challenging opportunity of working on a real risk modelling and risk management case in close collaboration with a Risk Center Partner Company. For the Spring 2019 Edition the Partner will be Zurich Insurance Group. | |||||

Objective | Students work on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the risk-related cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar students visit the partners’ headquarters, conduct interviews with members of the management team as well as internal and external experts, and present their results. | |||||

Content | Get a basic understanding of o The insurance and reinsurance business o Risk management and risk modelling o The role of operational risk management Get in contact with industry experts and conduct interviews on the topic. Conduct a small empirical study and present findings to the company | |||||

Prerequisites / Notice | Please apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than February 15, 2019. The number of participants is limited to 14. |

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