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- W1976601404 abstract "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." @default.
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- W1976601404 date "2010-07-01" @default.
- W1976601404 modified "2023-10-12" @default.
- W1976601404 title "Assessing different uncertainty measures of EBLUP: a resampling-based approach" @default.
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- W1976601404 doi "https://doi.org/10.1080/00949650902766860" @default.
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