Abstract Number:
1189
Submission Type:
Invited Paper Session
Participants:
Richard Samworth (1), Richard Samworth (1), Heather Battey (2), Yingying Fan (3), Tengyao Wang (1), Linjun Zhang (4)
Institutions:
(1) N/A, N/A, (2) Imperial College London, N/A, (3) University of Southern California, N/A, (4) Rutgers University, N/A
Chair:
Session Organizer:
Speaker(s):
Session Description:
The intention of this session is to showcase different aspects of modern statistical inference, combining high-dimensional statistics with statistical fairness, distribution-free inference and manifold-based inference. These represent some of the key themes that underpin the subject these days: the search for simplifying, explainable structures within a large ambient space, the incorporation of practical constraints into inferential procedures, and the desire to avoid the need to make unnecessary assumptions that often may not hold for complex data sets. The speakers are all outstanding early-mid career statisticians (and fantastic speakers), who are well-placed to articulate the most important issues and how their work advances the field.
Sponsors:
IMS 1
International Chinese Statistical Association 3
Royal Statistical Society 2
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
No
Applied
No
Estimated Audience Size
Medium (80-150)
I have read and understand that JSM participants must abide by the Participant Guidelines.
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I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
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