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Multiple testing and large-scale inference in Python

June 4 @ 2:00 pm - 4:00 pm

Rackham Building, Earl Lewis Room, 3rd Floor East

This workshop will cover techniques for conducting large-scale inference, using Python and its libraries.  We will cover the principals of how large scale inference is different from classical inference, and why multiple comparisons should usually be accounted for in an analysis.  We will then discuss the Bonferroni method, Benjamini and Hochberg’s False Discovery Rate (FDR), Efron’s local FDR, the Scheffe approach, and the knockoff filter.  The motivation for each approach will be covered, and the process for carrying it out in a data analysis will be discussed in detail.  Several case studies will be used to illustrate these analytic approaches.

Details

Date:
June 4
Time:
2:00 pm - 4:00 pm
Event Category:

Venue

Rackham Building, Earl Lewis Room, 3rd Floor East
915 E. Washington St.
Ann Arbor, MI 48109 United States

Organizer

CSCAR
Email:
cscar@umich.edu
Website:
cscar.research.umich.edu

Other

Register
Presenter(s)
Kerby Shedden