# Improving both domain and total area estimation by composition.

SORT (2004)

• Volume: 28, Issue: 1, page 69-86
• ISSN: 1696-2281

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## Abstract

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In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such a design, one fraction of the sample is distributed proportionally among the small areas and the rest is evenly distributed. Simulation is used to assess the performance of the direct estimator and two composite small area estimators, for a range of sample sizes and different sample distributions. Performance is measured in terms of mean squared errors for both small and large area parameters. Small area composite estimators open the possibility of reducing the sample size when the desired precision is given, or improving precision for a given sample size.

## How to cite

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Costa, Alex, Satorra, Albert, and Ventura, Eva. "Improving both domain and total area estimation by composition.." SORT 28.1 (2004): 69-86. <http://eudml.org/doc/40453>.

@article{Costa2004,
abstract = {In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such a design, one fraction of the sample is distributed proportionally among the small areas and the rest is evenly distributed. Simulation is used to assess the performance of the direct estimator and two composite small area estimators, for a range of sample sizes and different sample distributions. Performance is measured in terms of mean squared errors for both small and large area parameters. Small area composite estimators open the possibility of reducing the sample size when the desired precision is given, or improving precision for a given sample size.},
author = {Costa, Alex, Satorra, Albert, Ventura, Eva},
journal = {SORT},
keywords = {Regresión lineal; Estimación en áreas pequeñas; regional statistics; small areas; mean square error; direct and composite estimators},
language = {eng},
number = {1},
pages = {69-86},
title = {Improving both domain and total area estimation by composition.},
url = {http://eudml.org/doc/40453},
volume = {28},
year = {2004},
}

TY - JOUR
AU - Costa, Alex
AU - Satorra, Albert
AU - Ventura, Eva
TI - Improving both domain and total area estimation by composition.
JO - SORT
PY - 2004
VL - 28
IS - 1
SP - 69
EP - 86
AB - In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such a design, one fraction of the sample is distributed proportionally among the small areas and the rest is evenly distributed. Simulation is used to assess the performance of the direct estimator and two composite small area estimators, for a range of sample sizes and different sample distributions. Performance is measured in terms of mean squared errors for both small and large area parameters. Small area composite estimators open the possibility of reducing the sample size when the desired precision is given, or improving precision for a given sample size.
LA - eng
KW - Regresión lineal; Estimación en áreas pequeñas; regional statistics; small areas; mean square error; direct and composite estimators
UR - http://eudml.org/doc/40453
ER -

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