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Bootstrap for estimating the MSE of the Spatial EBLUP

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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 empirical best linear unbiased predictor (EBLUP). A simulation study based on the Italian 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.

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Authors and Affiliations

  1. Depto. de Estadística, Univ. Carlos III de Madrid, C/ Madrid, 126, 28903, Getafe, Spain

    Isabel Molina

  2. Dipto. di Statistica e Matematica Applicata all’Economia, Univ. di Pisa, Via C. Ridolfi, 10, 56124, Pisa, Italy

    Nicola Salvati & Monica Pratesi

Authors
  1. Isabel Molina
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  2. Nicola Salvati
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  3. Monica Pratesi
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Correspondence to Isabel Molina.

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Molina, I., Salvati, N. & Pratesi, M. Bootstrap for estimating the MSE of the Spatial EBLUP. Comput Stat 24, 441–458 (2009). https://doi.org/10.1007/s00180-008-0138-4

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  • DOI: https://doi.org/10.1007/s00180-008-0138-4

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