401-3620-20L  Student Seminar in Statistics: Inference in Some Non-Standard Regression Problems

SemesterAutumn Semester 2021
LecturersF. Balabdaoui
Periodicityevery semester recurring course
Language of instructionEnglish
CommentNumber 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.



Courses

NumberTitleHoursLecturers
401-3620-00 SStudent Seminar in Statistics: Inference in Some Non-Standard Regression Problems
Remark: former title in FS 2020: Student Seminar in Statistics: Inference in Non-Classical Regression Models
2 hrs
Mon16-18HG E 21 »
F. Balabdaoui

Catalogue data

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 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
LiteratureIn 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 / NoticeThe students need to be comfortable with regression models, classical estimation methods (Least squares, Maximum Likelihood estimation...), rates of convergence, asymptotic normality, etc.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersF. Balabdaoui
Typeungraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Admission requirementNot for students who already registered for this seminar (course unit 401-3620-20L) in the Spring Semester 2020.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

PlacesLimited number of places. Special selection procedure.
Beginning of registration periodRegistration possible from 02.08.2021
PriorityRegistration for the course unit is until 29.08.2021 only possible for the primary target group
Primary target groupData Science MSc (261000)
Mathematics BSc (404000) starting semester 05
Statistics MSc (436000)
Mathematics MSc (437000)
Applied Mathematics MSc (437100)
Mathematics (Mobility) (448000)
Waiting listuntil 27.09.2021
End of registration periodRegistration only possible until 17.09.2021

Offered in

ProgrammeSectionType
Data Science MasterSeminarWInformation
Mathematics BachelorSeminarsWInformation
Mathematics MasterSeminarsWInformation
Statistics MasterSeminar or Semester PaperWInformation