The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
213
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, August 1, 2011 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
IMS
|
| Abstract - #300101 |
|
Title:
|
Statistical Inference on Covariance Structure
|
|
Author(s):
|
Tony Cai*+
|
|
Companies:
|
University of Pennsylvania
|
|
Address:
|
, , ,
|
|
Keywords:
|
covariance matrix ;
minimax rate of convergence ;
optimal estimation ;
hypothesis testing ;
high-dimensional inference
|
|
Abstract:
|
Covariance structure is of fundamental importance in many areas of statistical inference and a wide range of applications. In the high dimensional setting where the dimension p can be much larger than the sample size n, classical methods and results based on fixed p and large n are no longer applicable. In this talk, I will discuss some new results on optimal estimation as well as testing the structure of large covariance matrices. The results and technical analysis reveal new features that are quite different from the conventional problems.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.