Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/1283
Título: Assessing different uncertainty measures of EBLUP: a resampling-based approach
Autor: Pereira, Luis Nobre
Coelho, Pedro S.
Palavras-chave: Bootstrap
Jackknife
MSPE of the EBLUP
Resampling methods
Small area estimation
Data: 2010
Editora: Taylor & Francis
Citação: Journal of Statistical Computation and Simulation, 80:7, 713-727
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.1/1283
ISSN: 1563-5163
Versão do Editor: http://www.tandfonline.com/doi/abs/10.1080/00949650902766860#preview
Aparece nas colecções:ESG2-Artigos (em revistas ou actas indexadas)

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