[Statlist] Reminder: ETH Young Data Science Researcher Seminar Zurich Virtual Seminar by Yuting Wei, Carnegie Mellon University

[Maurer Letizia]

Dear all

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"Reliable hypothesis testing paradigms in high dimensions"  
by Yuting Wei, Carnegie Mellon University

Time: Friday, 23 October 2020, 15:00-​16:00
Place: Zoom at https://ethz.zoom.us/j/92367940258

Abstract: Modern scientific discovery and decision making require the development of trustworthy and informative inferential procedures, which are particularly challenging when coping with high-dimensional data. This talk presents two vignettes on the theme of reliable high-dimensional inference.

The first vignette considers performing inference based on the Lasso estimator when the number of covariates is of the same order or larger than the number of observations. Classical asymptotic statistics theory fails due to two fundamental reasons: (1) The regularized risk is non-smooth; (2) The discrepancy between the estimator and the true parameter vector cannot be neglected. We pin down the distribution of the Lasso, as well as its debiased version, under a broad class of Gaussian correlated designs with non-singular covariance structure. Our findings suggest that a careful degree-of-freedom correction is crucial for computing valid confidence intervals in this challenging regime.

The second vignette investigates the Model-X knockoffs framework --- a general procedure that can leverage any feature importance measure to produce a variable selection algorithm. Model-X knockoffs rely on the construction of synthetic random variables and is, therefore, random. We propose a method for derandomizing --- and hence stabilizing --- model-X knockoffs. By aggregating the selection results across multiple runs of the knockoffs algorithm, our method provides stable decisions without compromising statistical power. Our approach, when applied to the multi-stage GWAS of prostate cancer, reports locations on the genome that have been replicated with other studies.

The first vignette is based on joint work with Michael Celentano and Andrea Montanari, whereas the second one is based on joint work with Zhimei Ren and Emmanuel Candes.
Best wishes,

M. Azadkia, Y. Chen, M. Löffler, A. Taeb

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html