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A pseudo‐empirical best linear unbiased prediction approach to small area estimation using survey weights
Author(s) -
You Yong,
Rao J. N. K.
Publication year - 2002
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316146
Subject(s) - best linear unbiased prediction , mathematics , mean squared error , small area estimation , estimator , bias of an estimator , statistics , minimum variance unbiased estimator , stein's unbiased risk estimate , efficient estimator , consistent estimator , consistency (knowledge bases) , computer science , artificial intelligence , selection (genetic algorithm) , geometry
The authors develop a small area estimation method using a nested error linear regression model and survey weights. In particular, they propose a pseudo‐empirical best linear unbiased prediction (pseudo‐EBLUP) estimator to estimate small area means. This estimator borrows strength across areas through the model and makes use of the survey weights to preserve the design consistency as the area sample size increases. The proposed estimator also has a nice self‐benchmarking property. The authors also obtain an approximation to the model mean squared error (MSE) of the proposed estimator and a nearly unbiased estimator of MSE. Finally, they compare the proposed estimator with the EBLUP estimator and the pseudo‐EBLUP estimator proposed by Prasad & Rao (1999), using data analyzed earlier by Battese, Harter & Fuller (1988).