Search result: Catalogue data in Spring Semester 2021

Statistics Master Information
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
NumberTitleTypeECTSHoursLecturers
401-3620-21LStudent Seminar in Statistics: Statistical Network Modeling Information Restricted registration - show details
Number of participants limited to 48.
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.
W4 credits2SP. L. Bühlmann, M. Azadkia
AbstractNetwork 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.
ObjectiveNetwork 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.
LiteratureE. 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.
Prerequisites / NoticeEvery 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.
401-3620-20LStudent Seminar in Statistics: Inference in Non-Classical Regression Models Restricted registration - show details
Does not take place this semester.
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.
W4 credits2SF. Balabdaoui
AbstractReview of some non-standard regression models and the statistical properties of estimation methods in such models.
ObjectiveThe 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).
ContentLinear 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 notesNo script is necessary for this seminar
LiteratureIn 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
401-4620-00LStatistics Lab Restricted registration - show details
Number of participants limited to 27.
W6 credits2SM. 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.
ContentStudents 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 notesn/a
LiteratureThe required literature will depend on the specific statistical problem
under investigation. Some introductory material can be found below.
Prerequisites / NoticePrerequisites:
Sound knowledge in basic statistical methods, especially regression and, if
possible, analysis of variance. Basic experience in Data Analysis with R.
401-3630-04LSemester Paper Restricted registration - show details
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
W4 credits6ASupervisors
AbstractSemester 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
401-3630-06LSemester Paper Restricted registration - show details
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
W6 credits9ASupervisors
AbstractSemester 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
363-1100-00LRisk Case Study Challenge Restricted registration - show details
Does not take place this semester.
W3 credits2S
AbstractThis 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.
ObjectiveDuring 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.
ContentStudents 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.
Prerequisites / NoticePlease apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than February 20, 2021.
The number of participants is limited to 16.
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