Indirect estimators for small areas use auxiliary variables to borrow strength from related areas through a linking model. Precision of indirect estimators depends on the validity of such a model. To protect against possible model failures, benchmarking procedures make the total of small area stimates match a design consistent estimate for a larger area. This is also particularly important for National Institutes of Statistics to ensure coherence between small area estimates and direct estimates produced at higher level planned domains. We investigate a self-benchmarked estimator in the case of a unit level logistic mixed model for a binary response, propose an estimator for its mean squared error and compare its performance with competing estimators through a simulation study.

A comparison of small area estimators of counts aligned with direct higher level estimates

MONTANARI, Giorgio Eduardo;RANALLI, Maria Giovanna;VICARELLI, CECILIA
2010

Abstract

Indirect estimators for small areas use auxiliary variables to borrow strength from related areas through a linking model. Precision of indirect estimators depends on the validity of such a model. To protect against possible model failures, benchmarking procedures make the total of small area stimates match a design consistent estimate for a larger area. This is also particularly important for National Institutes of Statistics to ensure coherence between small area estimates and direct estimates produced at higher level planned domains. We investigate a self-benchmarked estimator in the case of a unit level logistic mixed model for a binary response, propose an estimator for its mean squared error and compare its performance with competing estimators through a simulation study.
9788861295667
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/166741
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