On Mean Squared Prediction Error Estimation in Small Area Estimation Problems

2008 ◽  
Vol 37 (11) ◽  
pp. 1792-1798 ◽  
Author(s):  
Shijie Chen ◽  
P. Lahiri
Keyword(s):  
Error Estimation ◽  
Prediction Error ◽  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Mean Squared Prediction Error ◽  
Estimation Problems ◽  
Squared Prediction Error

A resampling variance estimator for the k nearest neighbours technique

2010 ◽  
Vol 40 (4) ◽  
pp. 648-658 ◽  
Author(s):  
S. Magnussen ◽  
R. E. McRoberts ◽  
E. O. Tomppo
Keyword(s):  
Monte Carlo ◽  
Small Area ◽  
Small Area Estimation ◽  
Computing Time ◽  
Monte Carlo Sampling ◽  
Cluster Sampling ◽  
Area Estimation ◽  
Area Of Interest ◽  
Nearest Neighbours ◽  
Estimation Problems

Current estimators of variance for the k nearest neighbours (kNN) technique are designed for estimates of population totals. Their efficiency in small-area estimation problems can be poor. In this study, we propose a modified balanced repeated replication estimator of variance (BRR) of a kNN total that performs well in small-area estimation problems and under both simple random and cluster sampling. The BRR estimate of variance is the sum of variances and covariances of unit-level kNN estimates in the area of interest. In Monte Carlo simulations of simple random and cluster sampling from seven artificial populations with real and simulated forest inventory data, the agreement between averages of BRR estimates of variance and Monte Carlo sampling variances was good both for population and for small-area totals. The modified BRR estimator is currently limited to sample sizes no larger than 1984. An accurate approximation to the proposed BRR estimator allows significant savings in computing time.

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level
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