Parametric bootstrap mean squared error of a small area multivariate EBLUP

Angelo Moretti, Natalie Shlomo, Joseph W. Sakshaug

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This article deals with mean squared error (MSE) estimation of a multivariate empirical best linear unbiased predictor (MEBLUP) under the unit-level multivariate nested-errors regression model for small area estimation via parametric bootstrap. A simulation study is designed to evaluate the performance of our algorithm and compare it with the univariate case bootstrap MSE which has been shown to be consistent to the true MSE. The simulation shows that, in line with the literature, MEBLUP provides unbiased estimates with lower MSE than EBLUP. We also provide a short empirical analysis based on real data collected from the U.S. Department of Agriculture.
Original languageEnglish
Pages (from-to)1474-1486
JournalCommunications in Statistics - Simulation and Computation
Volume49
Issue number6
DOIs
Publication statusPublished - 2 Jun 2020

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