e-space
Manchester Metropolitan University's Research Repository

    Parametric bootstrap mean squared error of a small area multivariate EBLUP

    Moretti, A ORCID logoORCID: https://orcid.org/0000-0001-6543-9418, Shlomo, N and Sakshaug, JW (2020) Parametric bootstrap mean squared error of a small area multivariate EBLUP. Communications in Statistics: Simulation and Computation, 49 (6). pp. 1474-1486. ISSN 0361-0918

    [img]
    Preview
    Accepted Version
    Download (1MB) | Preview

    Abstract

    © 2018, © 2018 Taylor & Francis Group, LLC. 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.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    298Downloads
    6 month trend
    130Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record