Assessing different uncertainty measures of EBLUP: A resampling-based approach

L. N. Pereira, Pedro Simões Coelho

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The empirical best linear unbiased prediction approach is a popular method for the estimation of small area parameters. However, the estimation of reliable means quared 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.

Original languageEnglish
Pages (from-to)713-727
Number of pages15
JournalJournal Of Statistical Computation And Simulation
Volume80
Issue number7
DOIs
Publication statusPublished - 1 Jul 2010

Keywords

  • Bootstrap
  • Jackknife
  • MSPE of the EBLUP
  • Resampling methods
  • Small area estimation

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