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Activity Number:
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339
- Official Statistics and Small Area Estimation
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #328903
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Title:
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Empirical Bayes Estimation of Small Area Means Under Unmatched Two-Fold Subarea Models
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Author(s):
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Song Cai* and Golshid Chatrchi and Shonosuke Sugasawa and J.N.K. Rao
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Companies:
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Carleton University and Carlelton University and The University of Tokyo and Carleton Univeristy
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Keywords:
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Best predictors;
EM algorithm;
MSE estimation;
Semiparametric bootstrap;
Small area estimation
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Abstract:
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We propose an empirical Bayes (EB) approach to estimation of small subarea means under a two-fold subarea-level model consisting of a sampling model and an unmatched linking model. The best predictors of subarea means are derived under general distributional assumptions on random effects of the linking model assuming the model parameters are known. The empirical best predictors, or EB estimators, of the subarea means then are obtained by plugging in the maximum likelihood estimators of the model parameters. In addition, a semiparametric bootstrap approach is proposed to estimate the mean squared error of the EB subarea estimator. The performance of the proposed estimators are evaluated by a simulation study.
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Authors who are presenting talks have a * after their name.
