(1) Depto. de Estadística, Univ. Carlos III de Madrid, C/ Madrid, 126, 28903 Getafe, Spain;(2) Dipto. di Statistica e Matematica Applicata all’Economia, Univ. di Pisa, Via C. Ridolfi, 10, 56124 Pisa, Italy
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.