A small area predictor under area-level linear mixed models with restrictions

Luis N. Pereira, Pedro S. Coelho

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

A calibrated small area predictor based on an area-level linear mixed model with restrictions is proposed. It is showed that such restricted predictor, which guarantees the concordance between the small area estimates and a known estimate at the aggregate level, is the best linear unbiased predictor. The mean squared prediction error of the calibrated predictor is discussed. Further, a restricted predictor under a particular time-series and cross-sectional model is presented. Within a simulation study based on real data collected from a longitudinal survey conducted by a national statistical office, the proposed estimator is compared with other competitive restricted and non-restricted predictors.

Original languageEnglish
Pages (from-to)2524-2544
Number of pages21
JournalCommunications in Statistics - Theory and Methods
Volume41
Issue number13-14
DOIs
Publication statusPublished - 1 Jul 2012

Keywords

  • Best linear unbiased prediction
  • Calibrated predictor
  • Linear mixed model
  • Mean squared prediction error of the calibrated predictor
  • Small area estimation

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