Abstract Details
Activity Number:
|
471
|
Type:
|
Topic Contributed
|
Date/Time:
|
Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract - #309172 |
Title:
|
Likelihood Inference in Small-Area Estimation Using P-Spline and Time Series Models
|
Author(s):
|
Farhad Shokoohi*+ and Mahmoud Torabi
|
Companies:
|
University of Manitoba and University of Manitoba
|
Keywords:
|
Bayesian computation ;
Exponential family ;
Penalized spline ;
Prediction interval ;
Random effect ;
Time series
|
Abstract:
|
Nonparametric methods, despite their advantages, have not been often used in Small Area Estimation (SAE) due to methodological difficulties. Recently, a nonparametric linear model using cross-sectional data was introduced in SAE. However, there are many real applications in SAE which are time-related as well. In this talk, we introduce non-parametric models for Normal and non-Normal data situations with incorporating cross-sectional and time-series data. Frequentist analysis of these models is computationally difficult. Recent method of data cloning has overcome computational difficulties of mixed models from frequentist perspective. We demonstrate how the data cloning approach can be used to perform frequentist analysis of these complex models for both continuous and discrete data. One advantage of the data cloning approach is that both prediction and prediction intervals are easily obtained. The performance of the proposed approach is evaluated through several simulation studies and also by a real application.
|
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.