Abstract Details
|
Activity Number:
|
681
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
| Abstract - #309002 |
|
Title:
|
Bayesian Multivariate Estimate of Global Temperature Trends
|
|
Author(s):
|
Shawn Ni*+ and Dongchu Sun
|
|
Companies:
|
Univ of Missouri - Columbia and University of Missouri
|
|
Keywords:
|
smoothing spline ;
vector ;
Bayesian
|
|
Abstract:
|
In this paper, we consider a multivariate smoothing problem with correlated errors and correlated derivatives of the curves. Full Bayesian inference is introduced for the smoothing spline, the unknown covariance matrix of the errors and a symmetric smoothing parameter matrix. A prior is proposed on the symmetric smoothing parameter matrix, and Markov Chain Monte Carlo (MCMC) algorithms are developed for Bayesian computation. The proposed method is then applied to estimate the trends of abnormal surface temperature in ten geographical zones.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 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.
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.