Legend: Boston Convention & Exhibition Center = CC, Westin Boston Waterfront = W, Seaport Boston Hotel = S
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
|
|
Tweet | ||
| 215 * ! | Mon, 8/4/2014, 2:00 PM - 3:50 PM | CC-259A | |
| New Statistical Methodology on Supervised and Unsupervised Learning Problems — Topic Contributed Papers | |||
| WNAR , ENAR , Section on Physical and Engineering Sciences | |||
| Organizer(s): Jingyi Jessica Li, University of California, Los Angeles | |||
| Chair(s): Jingyi Jessica Li, University of California, Los Angeles | |||
| 2:05 PM | A Plug-In Approach to Supervised Binary Classification Problems in High-Dimensional Space Under the Neyman-Pearson Paradigm — Anqi Zhao, Harvard ; Xin Tong, University of Southern California ; Yang Feng, Columbia University ; Lie Wang, MIT | ||
| 2:25 PM | Kernel-Based Measures of Association — Ying Liu, Columbia University ; Victor de la Pena, Columbia University ; Tian Zheng, Columbia University | ||
| 2:45 PM | Inferring Gene Functional Modules and Association Networks Using Sparse Canonical Correlation Analysis — Haiyan Huang | ||
| 3:05 PM | A Plug-In Approach to Neyman-Pearson Classification — Xin Tong, University of Southern California | ||
| 3:25 PM | Equitability and the Maximal Information Coefficient — David Reshef, MIT/Harvard ; Yakir Reshef, Harvard/MIT ; Pardis Sabeti, Harvard ; Michael Mitzenmacher, Harvard | ||
| 3:45 PM | Floor Discussion | ||
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.