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Small Area Estimation by Splitting the Sampling Weights
Date de parution
2013-7-1
In
Electronic Journal of Statistics
No
7
De la page
1835
A la page
1855
Résumé
A new method is proposed for small area estimation. The prin-
ciple is based upon the splitting of the sampling weights between the areas. A matrix of weights is defined. Each column of this matrix enables us to estimate the total of the variables of interest at the level of an area. This method automatically satisfies the coherence property between the local estimates and the overall estimate. Moreover, the local estimators are calibrated on auxiliary information available at the level of the small areas. This methodology also enables the use of composite estimators that are weighted means between a direct estimator and a synthetic estimator. Once the weights are computed, the estimates can be easily computed for any variable of interest. A set of simulations shows the interest of the proposed method.
ciple is based upon the splitting of the sampling weights between the areas. A matrix of weights is defined. Each column of this matrix enables us to estimate the total of the variables of interest at the level of an area. This method automatically satisfies the coherence property between the local estimates and the overall estimate. Moreover, the local estimators are calibrated on auxiliary information available at the level of the small areas. This methodology also enables the use of composite estimators that are weighted means between a direct estimator and a synthetic estimator. Once the weights are computed, the estimates can be easily computed for any variable of interest. A set of simulations shows the interest of the proposed method.
Autre version
http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ejs/1373461823
Type de publication
Resource Types::text::journal::journal article
