This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and non- parametric bootstrap procedures are proposed for estimating the mean squared error of the Empirical Best Linear Unbiased Predictor (EBLUP). A simulation study based on the Ital- ian Agriculture Census 2000 compares bootstrap and analytical estimates of the MSE and studies their robustness to non-normality. Results indicate lower bias for the non-parametric bootstrap under specific departures from normality.

Bootstrap for estimating the mean squared error of the Spatial Eblup

SALVATI, NICOLA;PRATESI, MONICA
2009-01-01

Abstract

This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and non- parametric bootstrap procedures are proposed for estimating the mean squared error of the Empirical Best Linear Unbiased Predictor (EBLUP). A simulation study based on the Ital- ian Agriculture Census 2000 compares bootstrap and analytical estimates of the MSE and studies their robustness to non-normality. Results indicate lower bias for the non-parametric bootstrap under specific departures from normality.
2009
Molina, I; Salvati, Nicola; Pratesi, Monica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/131130
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