Small area estimation via unmatched sampling and linking models
SUGASAWA, Shonosuke - Personal Name
KUBOKAWA, Tatsuya - Personal Name
RAO, J. N. K. - Personal Name
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.
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.
Availability
| EB00000004413K | Available |
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Content Type
E-Jurnal
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Subject(s)
Bootstrap
Mean squared error
Empirical Bayes
Expectation–maximization algorithm
Fay–Herriot model
Penalized spline
Specific Detail Info
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Statement of Responsibility
Shonosuke Sugasawa 1, Tatsuya Kubokawa 2, J. N. K. Rao 3
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