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:
|
523
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
| Abstract - #300902 |
|
Title:
|
Comparing Methods of Likelihood-Based Approximate Estimation for High-Dimensional Spatio-Temporal Covariances
|
|
Author(s):
|
Yun Bai*+ and Peter Song
|
|
Companies:
|
University of Michigan and University of Michigan
|
|
Address:
|
Department of Biostatistics, , MI, 48109,
|
|
Keywords:
|
high-dimensional ;
approximate likelihood ;
spatio-temporal ;
covariance tapring ;
composite likelihood
|
|
Abstract:
|
In the recent literature, a number of approximate estimation methods have been proposed to achieve computational feasibility when dealing with high-dimensional covariance matrices for massive spatial and/or spatio-temporal processes. Some popular approaches include covariance tapering, conditional and marginal composite likelihood approaches, and spectral approximations, among others. To date, each of these methods has been only studied in detail in their respective papers, and it remains unknown regarding relative performances of these methods. It is natural to address the understanding of pros and cons of these methods by thorough comparisons. In this paper, based on a comprehensive simulation-based comparison, we report merits and limits of these methods under different settings of spatial and temporal dependence structures.
|
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.