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Assessing different uncertainty measures of EBLUP: a resampling-based approach
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Orientador(es)
Resumo(s)
The empirical best linear unbiased prediction approach is a popular method for the estimation of small area parameters. However, the estimation of reliable mean squared prediction error (MSPE) of the estimated best
linear unbiased predictors (EBLUP) is a complicated process. In this paper we study the use of resampling
methods for MSPE estimation of the EBLUP. A cross-sectional and time-series stationary small area
model is used to provide estimates in small areas. Under this model, a parametric bootstrap procedure
and a weighted jackknife method are introduced. A Monte Carlo simulation study is conducted in order
to compare the performance of different resampling-based measures of uncertainty of the EBLUP with
the analytical approximation. Our empirical results show that the proposed resampling-based approaches
performed better than the analytical approximation in several situations, although in some cases they tend
to underestimate the true MSPE of the EBLUP in a higher number of small areas.
Descrição
Palavras-chave
Bootstrap Jackknife MSPE of the EBLUP Resampling methods Small area estimation
Contexto Educativo
Citação
Journal of Statistical Computation and Simulation, 80:7, 713-727