Small area estimation under Fay–Herriot models with non-parametric estimation of heteroscedasticity

Fay–Herriot models relate direct estimators of small area means to vectors of area-level auxiliary covariates. Estimation of error variances in these models is a problem because of the lack of data within areas. A non-parametric approach is proposed for estimating these variances. Estimators of the...

Full description

Saved in:
Bibliographic Details
Published in:Statistical modelling 2010-07, Vol.10 (2), p.215-239
Main Authors: González-Manteiga, W, Lombarda, MJ, Molina, I, Morales, D, Santamaría, L
Format: Article
Language:English
Subjects:
Bootstrap method
Estimating techniques
Expected values
Experiments
Labor market
Mathematical models
Mean square errors
Population
Sampling error
Simulation
Statistical analysis
Studies
Unemployment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fay–Herriot models relate direct estimators of small area means to vectors of area-level auxiliary covariates. Estimation of error variances in these models is a problem because of the lack of data within areas. A non-parametric approach is proposed for estimating these variances. Estimators of the remaining model parameters are derived and their asymptotic properties are studied. Moreover, small area estimators that incorporate the estimated error variances are obtained and several simple estimators of the mean squared error of these estimators are proposed. Simulation experiments study the small sample performance of the new small area estimators and compare the different estimators of the mean squared errors. Finally, the results are applied to the estimation of unemployment proportions in Spanish domains.
ISSN:1471-082X
1477-0342
DOI:10.1177/1471082X0801000206