[Sds-seminars] S&DS In-Person Seminar, Zhimei Ren, 2/20, 4pm-5pm, "Stable Variable Selection with Knockoffs"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science  

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (
<https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f
8b73c34-a27b-42a7-a073-af2d00f90ffa>
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8
b73c34-a27b-42a7-a073-af2d00f90ffa)

 <https://0.0.0.10/> 3:30pm -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue 


ZHIMEI REN, University of Chicago




Date: Monday, February 20, 2023

Time: 4:00PM to 5:00PM

Location: Mason Lab 211
<http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us
> see map 

9 Hillhouse Ave

New Haven, CT 06511

 <https://zhimeir.github.io/> Website 

 

Title: Stable Variable Selection with Knockoffs

 

Information and Abstract: 

A common problem in many modern statistical applications is to find a set of
important variables-from a pool of many candidates-that explain the response
of interest. For this task, model-X knockoffs offers a general framework
that can leverage any feature importance measure to produce a variable
selection algorithm: it discovers true effects while rigorously controlling
the number or fraction of false positives, paving the way for reproducible
scientific discoveries. The model-X knockoffs, however, is a randomized
procedure that relies on the one-time construction of synthetic (random)
variables. Different runs of model-X knockoffs on the same dataset often
result in different sets of selected variables, which is not desirable for
the reproducibility of the reported results.

In this talk, I will introduce derandomization schemes that aggregate the
selection results across multiple runs of the knockoffs algorithm to yield
stable selection. In the first part, I will present a derandomization scheme
that controls the number of false positives, i.e., the per family error rate
(PFER) and the k family-wise error rate (k-FWER). In the second part, I will
talk about an alternative derandomization scheme with provable false
discovery rate (FDR) control. Equipped with these derandomization steps, the
knockoffs framework provides a powerful tool for making reproducible
scientific discoveries. The proposed methods are evaluated on both simulated
and real data, demonstrating comparable power and dramatically lower
selection variability when compared with the original model-X knockoffs.

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (
<https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f
8b73c34-a27b-42a7-a073-af2d00f90ffa>
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8
b73c34-a27b-42a7-a073-af2d00f90ffa)

 <https://0.0.0.10/> 3:30pm -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue 

 

For more details and upcoming events visit our website at
<http://statistics.yale.edu/> http://statistics.yale.edu/

 

Department of Statistics and Data Science

Yale University
24 Hillhouse Avenue
New Haven, CT 06511

t 203.432.0666
f 203.432.0633

 

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