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Bootstrap for estimating the mean squared error of the spatial EBLUP

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Listed:
  • Molina, Isabel
  • Salvati, Nicola
  • Pratesi, Monica

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 nonparametric bootstrap procedures are proposed for estimating the mean squared error of the EBLUP (Empirical Best Linear Unbiased Predictor). A simulation study compares the bootstrap estimates with an asymptotic analytical approximation and studies the robustness to non-normality. Finally, two applications with real data are described.

Suggested Citation

  • Molina, Isabel & Salvati, Nicola & Pratesi, Monica, 2007. "Bootstrap for estimating the mean squared error of the spatial EBLUP," DES - Working Papers. Statistics and Econometrics. WS ws073408, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws073408
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    References listed on IDEAS

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    1. Gonzalez-Manteiga, W. & Lombardia, M.J. & Molina, I. & Morales, D. & Santamaria, L., 2007. "Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2720-2733, February.
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    Keywords

    Spatial correlation;

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