A comparative study of small area estimators.

Santamaría, Laureano, Morales, Domingo, and Molina, Isabel. "A comparative study of small area estimators.." SORT 28.2 (2004): 215-230. <http://eudml.org/doc/40461>.

@article{Santamaría2004,
abstract = {It is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model-based estimators borrow strength from other related areas to avoid this problem. How small should domain sample sizes be to recommend the use of model-based estimators? How robust are small area estimators with respect to the rate sample size/number of domains?To give answers or recommendations about the questions above, a Monte Carlo simulation experiment is carried out. In this simulation study, model-based estimators for small areas are compared with some standard design-based estimators. The simulation study starts with the construction of an artificial population data file, imitating a census file of a Statistical Office. A stratified random design is used to draw samples from the artificial population. Small area estimators of the mean of a continuous variable are calculated for all small areas and compared by using different performance measures. The evolution of these performance measures is studied when increasing the number of small areas, which means to decrease their sizes.},
author = {Santamaría, Laureano, Morales, Domingo, Molina, Isabel},
journal = {SORT},
keywords = {Teoría de muestras; Estimación en áreas pequeñas; Inferencia paramétrica; Estimador puntual; Simulación de Montecarlo; small area estimation; eblup estimators; sampling designs; mixed linear models},
language = {eng},
number = {2},
pages = {215-230},
title = {A comparative study of small area estimators.},
url = {http://eudml.org/doc/40461},
volume = {28},
year = {2004},
}

TY - JOUR
AU - Santamaría, Laureano
AU - Morales, Domingo
AU - Molina, Isabel
TI - A comparative study of small area estimators.
JO - SORT
PY - 2004
VL - 28
IS - 2
SP - 215
EP - 230
AB - It is known that direct-survey estimators of small area parameters, calculated with the data from the given small area, often present large mean squared errors because of small sample sizes in the small areas. Model-based estimators borrow strength from other related areas to avoid this problem. How small should domain sample sizes be to recommend the use of model-based estimators? How robust are small area estimators with respect to the rate sample size/number of domains?To give answers or recommendations about the questions above, a Monte Carlo simulation experiment is carried out. In this simulation study, model-based estimators for small areas are compared with some standard design-based estimators. The simulation study starts with the construction of an artificial population data file, imitating a census file of a Statistical Office. A stratified random design is used to draw samples from the artificial population. Small area estimators of the mean of a continuous variable are calculated for all small areas and compared by using different performance measures. The evolution of these performance measures is studied when increasing the number of small areas, which means to decrease their sizes.
LA - eng
KW - Teoría de muestras; Estimación en áreas pequeñas; Inferencia paramétrica; Estimador puntual; Simulación de Montecarlo; small area estimation; eblup estimators; sampling designs; mixed linear models
UR - http://eudml.org/doc/40461
ER -