Back to Search
Start Over
ROBUST ESTIMATION OF SMALL-AREA MEANS AND QUANTILES.
- Source :
-
Australian & New Zealand Journal of Statistics . Jun2010, Vol. 52 Issue 2, p167-186. 20p. 2 Diagrams, 6 Charts, 2 Graphs. - Publication Year :
- 2010
-
Abstract
- Small-area estimation techniques have typically relied on plug-in estimation based on models containing random area effects. More recently, regression M-quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug-in M-quantile estimator for the small-area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small-area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small-area estimation, based on representing a small-area estimator as a functional of a predictor of this small-area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small-area means and quantiles and is not restricted to M-quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model-based and design-based simulations, with the latter using economic data collected in an Australian farm survey. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ROBUST statistics
*ESTIMATION theory
*MATHEMATICAL models
*AGRICULTURAL statistics
Subjects
Details
- Language :
- English
- ISSN :
- 13691473
- Volume :
- 52
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Australian & New Zealand Journal of Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 50868067
- Full Text :
- https://doi.org/10.1111/j.1467-842X.2010.00572.x