Non-parametric bootstrap mean squared error estimation for m-quantile estimators of small area averages, quantiles and poverty indicators
Non-parametric bootstrap mean squared error estimation for m-quantile estimators of small area averages, quantiles and poverty indicators
Small area estimation is conventionally concerned with the estimation of small area averages and totals. More recently emphasis has been also placed on the estimation of poverty indicators and of key quantiles of the small area distribution function using robust models for example, the M-quantile small area model (Chambers and Tzavidis, 2006). In parallel to point estimation, Mean Squared Error (MSE) estimation is an equally crucial and challenging task. However, while analytic MSE estimation for small area averages is possible, analytic MSE estimation for quantiles and poverty indicators is extremely difficult. Moreover, one of the main criticisms of the analytic MSE estimator for M-quantile estimates of small area averages proposed by Chambers and Tzavidis (2006) and Chambers et al. (2009) is that it can be unstable when the area-specific sample sizes are small.
chambers-dunstan estimator, income distribution, domain estimation, poverty mapping, resampling methods, robust estimation
Southampton Statistical Sciences Research Institute, University of Southampton
Marchetti, Stefano
d47d90a9-90d3-40fa-b290-322caf8ee283
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Pratesi, Monica
d7fd7c86-3f2d-42ca-826c-c6d0f0a2a00a
2 March 2011
Marchetti, Stefano
d47d90a9-90d3-40fa-b290-322caf8ee283
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Pratesi, Monica
d7fd7c86-3f2d-42ca-826c-c6d0f0a2a00a
Marchetti, Stefano, Tzavidis, Nikos and Pratesi, Monica
(2011)
Non-parametric bootstrap mean squared error estimation for m-quantile estimators of small area averages, quantiles and poverty indicators
(S3RI Methodology Working Papers, M11/02)
Southampton, GB.
Southampton Statistical Sciences Research Institute, University of Southampton
34pp.
Record type:
Monograph
(Working Paper)
Abstract
Small area estimation is conventionally concerned with the estimation of small area averages and totals. More recently emphasis has been also placed on the estimation of poverty indicators and of key quantiles of the small area distribution function using robust models for example, the M-quantile small area model (Chambers and Tzavidis, 2006). In parallel to point estimation, Mean Squared Error (MSE) estimation is an equally crucial and challenging task. However, while analytic MSE estimation for small area averages is possible, analytic MSE estimation for quantiles and poverty indicators is extremely difficult. Moreover, one of the main criticisms of the analytic MSE estimator for M-quantile estimates of small area averages proposed by Chambers and Tzavidis (2006) and Chambers et al. (2009) is that it can be unstable when the area-specific sample sizes are small.
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Published date: 2 March 2011
Keywords:
chambers-dunstan estimator, income distribution, domain estimation, poverty mapping, resampling methods, robust estimation
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Local EPrints ID: 176003
URI: http://eprints.soton.ac.uk/id/eprint/176003
PURE UUID: e5a69063-4313-4ae0-a80e-6e6735e6f9cc
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Date deposited: 02 Mar 2011 11:17
Last modified: 14 Mar 2024 02:46
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Contributors
Author:
Stefano Marchetti
Author:
Monica Pratesi
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