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Non-parametric bootstrap mean squared error estimation for m-quantile estimators of small area averages, quantiles and poverty indicators

Record type: Monograph (Working Paper)

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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Citation

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 , Southampton, GB Southampton Statistical Sciences Research Institute 34pp. (S3RI Methodology Working Papers, M11/02).

More information

Published date: 2 March 2011
Keywords: chambers-dunstan estimator, income distribution, domain estimation, poverty mapping, resampling methods, robust estimation

Identifiers

Local EPrints ID: 176003
URI: http://eprints.soton.ac.uk/id/eprint/176003
PURE UUID: e5a69063-4313-4ae0-a80e-6e6735e6f9cc

Catalogue record

Date deposited: 02 Mar 2011 11:17
Last modified: 18 Jul 2017 12:08

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Contributors

Author: Stefano Marchetti
Author: Nikos Tzavidis
Author: Monica Pratesi

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