Abstract Details
Activity Number:
|
311
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
|
Sponsor:
|
IMS
|
Abstract #315871
|
View Presentation
|
Title:
|
Nonparametric Estimates of Correlation Matrices via Block Thresholding
|
Author(s):
|
Linjun Zhang* and Tony Cai
|
Companies:
|
University of Pennsylvania and University of Pennsylvania
|
Keywords:
|
Adaptive estimation ;
block thresholding ;
correlation matrix ;
spectral norm ;
minimax estimation
|
Abstract:
|
In this paper we consider correlation matrix estimation in a nonparametric fashion. In order to be adaptive to a generalized parameter space, we propose a block thresholding scheme by carefully dividing the correlation matrix. To prove the result to be optimally minimax, we came up with a more friendly version of convergence rate for nonparametric correlation estimation, this proof is relatively short and the inequality is slightly sharper than previous results. In addition, we propose applications to illustrate our idea.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, 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.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.