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:
|
174
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 1, 2011 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Survey Research Methods
|
Abstract - #302348 |
Title:
|
An Empirical Best Linear Unbiased Prediction Approach to Small-Area Estimation of Crop Parameters
|
Author(s):
|
Michael E. Bellow*+ and Partha Lahiri
|
Companies:
|
U.S. Department of Agriculture and University of Maryland
|
Address:
|
NASS, Fairfax, VA, 22031,
|
Keywords:
|
small-area estimation ;
components of variance ;
predictor variables
|
Abstract:
|
Accurate county (small-area) level estimation of crop and livestock items is an important priority for the USDA's National Agricultural Statistics Service (NASS). We consider an empirical best linear unbiased prediction (EBLUP) method for combining multiple data sources to estimate crop harvested area (and potentially other crop parameters) at the county level. This method assumes a linear mixed model that relates survey reported harvested area to both unit (farm) and area (county) level covariates, with variance components estimated using a technique which ensures strictly positive consistent estimation of the model variance. A parametric bootstrap method that incorporates all sources of uncertainty can be used to estimate variability parameters. Results of a study comparing the proposed EBLUP method with standard ratio and regression type estimators and a synthetic estimator for corn and soybeans in seven states in the Midwestern grain belt region of the US are discussed.
|
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.