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Robust estimation of small-area means and quantiles

Robust estimation of small-area means and quantiles
Robust estimation of small-area means and quantiles
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.
australian farm data, chambers–dunstan estimator, finite-population distribution function, m-quantile regression, rao–kovar–mantel estimator, robust regression, small-area estimation, smearing estimator
1369-1473
167-186
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Marchetti, Stefano
d47d90a9-90d3-40fa-b290-322caf8ee283
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Marchetti, Stefano
d47d90a9-90d3-40fa-b290-322caf8ee283
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2

Tzavidis, Nikos, Marchetti, Stefano and Chambers, Ray (2010) Robust estimation of small-area means and quantiles. Australian & New Zealand Journal of Statistics, 52 (2), 167-186. (doi:10.1111/j.1467-842X.2010.00572.x).

Record type: Article

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.

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More information

Published date: June 2010
Keywords: australian farm data, chambers–dunstan estimator, finite-population distribution function, m-quantile regression, rao–kovar–mantel estimator, robust regression, small-area estimation, smearing estimator
Organisations: Social Statistics

Identifiers

Local EPrints ID: 181889
URI: http://eprints.soton.ac.uk/id/eprint/181889
ISSN: 1369-1473
PURE UUID: 70258726-0935-4276-9e59-d099cd374e55
ORCID for Nikos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095

Catalogue record

Date deposited: 19 Apr 2011 14:40
Last modified: 15 Mar 2024 03:11

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Contributors

Author: Nikos Tzavidis ORCID iD
Author: Stefano Marchetti
Author: Ray Chambers

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