The authors use an empirical Bayes (EB) approach to small area estimation
under area-level unmatched sampling and linking models. Model parameters are esti-
mated by a unified expectation and maximization (EM) algorithm and used to obtain
EB estimators of area parameters. Results are extended to a nonparametric linking
model based on a spline approximation. Approximate EB estimators that are compu-
tationally simpler are also obtained. Different bootstrap approaches to estimating the
mean squared error (MSE) of the EB estimators are proposed. Results of a simulation
study on the performance of the proposed methods are presented. Proposed methods
are applied to data from a survey of family income and expenditure in Japan and
poverty rates in Spanish provinces.

EB00000004413K

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• Small area estimation via unmatched sampling and linking models