An adjusted maximum likelihood method for solving small area estimation problems
Abstract
For the well-known Fay-Herriot small area model, standard variance component estimation methods frequently produce zero estimates of the strictly positive model variance. As a consequence, an empirical best linear unbiased predictor of a small area mean, commonly used in small area estimation, could reduce to a simple regression estimator, which typically has an overshrinking problem. We propose an adjusted maximum likelihood estimator of the...
Paper Details
Title
An adjusted maximum likelihood method for solving small area estimation problems
Published Date
Apr 1, 2010
Volume
101
Issue
4
Pages
882 - 892
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