Empirical bootstrap bias correction and estimation of
prediction mean square error in small area estimation
Empirical bootstrap bias correction and estimation of
prediction mean square error in small area estimation
We develop a method for bias correction, which models the error of the target estimator as a function of the corresponding estimator obtained from bootstrap samples, and the original estimators and bootstrap estimators of the parameters governing the model fitted to the sample data. This is achieved by considering a number of plausible parameter values, generating a pseudo original sample for each parameter and bootstrap samples for each such sample, and then searching for an appropriate functional relationship. Under certain conditions, the procedure also permits estimation of the mean square error of the bias corrected estimator. The method is applied for estimating the prediction mean square error in small area estimation of proportions under a generalized mixed model. Empirical comparisons with jackknife and bootstrap methods are presented.
best predictor, crossvalidation, mpirical best predictor, generalized mixed model, jackknife, order of bias, parametric bootstrap
457-472
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Correa, Solange
7863f596-e178-4429-9c46-cf07a1962048
6 April 2012
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Correa, Solange
7863f596-e178-4429-9c46-cf07a1962048
Pfeffermann, Danny and Correa, Solange
(2012)
Empirical bootstrap bias correction and estimation of
prediction mean square error in small area estimation.
Biometrika, 99 (2), .
(doi:10.1093/biomet/ass010).
Abstract
We develop a method for bias correction, which models the error of the target estimator as a function of the corresponding estimator obtained from bootstrap samples, and the original estimators and bootstrap estimators of the parameters governing the model fitted to the sample data. This is achieved by considering a number of plausible parameter values, generating a pseudo original sample for each parameter and bootstrap samples for each such sample, and then searching for an appropriate functional relationship. Under certain conditions, the procedure also permits estimation of the mean square error of the bias corrected estimator. The method is applied for estimating the prediction mean square error in small area estimation of proportions under a generalized mixed model. Empirical comparisons with jackknife and bootstrap methods are presented.
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Published date: 6 April 2012
Keywords:
best predictor, crossvalidation, mpirical best predictor, generalized mixed model, jackknife, order of bias, parametric bootstrap
Organisations:
Social Statistics & Demography
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Local EPrints ID: 191975
URI: http://eprints.soton.ac.uk/id/eprint/191975
ISSN: 0006-3444
PURE UUID: e745fcb4-24a1-4d48-972d-7f3fcc165410
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Date deposited: 28 Jun 2011 15:02
Last modified: 14 Mar 2024 03:48
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Author:
Solange Correa
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