Clustering in small area estimation with area level linear mixed models
Volume: 180, Issue: 4, Pages: 1253 - 1279
Published: Oct 1, 2017
Abstract
Summary
Finding reliable estimates of parameters of subpopulations (areas) in small area estimation is an important problem especially when there are few or no samples in some areas. Clustering small areas on the basis of the Euclidean distance between their corresponding covariates is proposed to obtain smaller mean-squared prediction error (MSPE) for the predicted values of area means by using area level linear mixed models. We first propose...
Paper Details
Title
Clustering in small area estimation with area level linear mixed models
Published Date
Oct 1, 2017
Volume
180
Issue
4
Pages
1253 - 1279
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Notes
History