Non-parametric bootstrap mean squared error estimation for m-quantile estimators of small area averages, quantiles and poverty indicators
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).
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.
|Item Type:||Monograph (Working Paper)|
|Keywords:||chambers-dunstan estimator, income distribution, domain estimation, poverty mapping, resampling methods, robust estimation|
|Subjects:||H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
|Divisions:||University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
|Date Deposited:||02 Mar 2011 11:17|
|Last Modified:||27 Mar 2014 19:22|
|Publisher:||Southampton Statistical Sciences Research Institute|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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