Abstract Details
Activity Number:
|
293
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract - #308039 |
Title:
|
A Model-Dependent Ratio Estimator of Variance for Two-Stage with Regression Designs
|
Author(s):
|
Steen Magnussen*+ and Erik Næsset and Terje Gobakken
|
Companies:
|
Canadian Forest Service and Norwegian University of Life Sciences and Norwegian University of Life Sciences
|
Keywords:
|
sampling design ;
ratio-to-size estimator ;
forest inventory ;
cluster sampling ;
large scale survey
|
Abstract:
|
Design-based estimators for two-stage SRS with regression can suffer from a lack of precision (efficiency) when the primary sampling units (PSU) vary in size and PSU-totals are approximately proportional to the size of a PSU. Precision may deteriorate further for domain-specific estimators when PSUs contain elements from different domains. Model-dependent ratio-to-size estimators are more efficient. This study introduces a new model-dependent variance estimator for a two-stage ratio estimator. The new estimator of variance is derived from a single stage estimator of a variance of a ratio under the assumptions: i) the target variable (y) is approximately equal to the sum of a model-dependent prediction and two error terms capturing lack-of-fit and model-errors, and ii) an unbiased estimate of the true model parameters. Simulations confirmed the negative effects of unequal PSU sizes on the precision of design-based estimators and a superior performance of the proposed estimator of variance. Analysis of a regional two-stage survey of forest biomass corroborated these findings.
|
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.