|
Activity Number:
|
224
|
|
Type:
|
Invited
|
|
Date/Time:
|
Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Survey Research Methods
|
| Abstract - #307998 |
|
Title:
|
On Small-Area Prediction Interval Problems
|
|
Author(s):
|
Snigdhansu Chatterjee and Partha Lahiri*+ and Huilin Li
|
|
Companies:
|
The University of Minnesota and University of Maryland and University of Maryland
|
|
Address:
|
JPSM, College Park, 20742,
|
|
Keywords:
|
Predictive distribution ; Linear mixed model ; Bootstrap ; Coverage accuracy ; Empirical Best Linear Unbiased Predictor
|
|
Abstract:
|
Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like undercoverage, excessive length and lack of interpretability. We propose a resampling driven approach. Simulation results demonstrate the superiority of our method over the existing ones.
|
