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    Variance component estimation uncertainty for unbalanced data: Application to a continent-wide vertical datum

    202735_202735.pdf (919.8Kb)
    Access Status
    Open access
    Authors
    Filmer, Michael
    Featherstone, Will
    Claessens, Sten
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Filmer, M. and Featherstone, W. and Claessens, S. 2014. Variance component estimation uncertainty for unbalanced data: Application to a continent-wide vertical datum. Journal of Geodesy. 88 (11): pp. 1081-1093.
    Source Title
    Journal of Geodesy
    DOI
    10.1007/s00190-014-0744-6
    ISSN
    09497714
    School
    Department of Spatial Sciences
    Remarks

    The final publication is available at Springer via http://doi.org/10.1007/s00190-014-0744-6

    NOTICE: This is the author’s version of a work in which changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.

    URI
    http://hdl.handle.net/20.500.11937/29800
    Collection
    • Curtin Research Publications
    Abstract

    Variance component estimation (VCE) is used to update the stochastic model in least-squares adjustments, but the uncertainty associated with the VCE-derived weights is rarely considered. Unbalanced data is where there is an unequal number of observations in each heterogeneous dataset comprising the variance component groups. As a case study using highly unbalanced data, we redefine a continent-wide vertical datum from a combined least-squares adjustment using iterative VCE and its uncertainties to update weights for each data set. These are: (1) a continent-wide levelling network, (2) a model of the ocean’s mean dynamic topography and mean sea level observations, and (3) GPS-derived ellipsoidal heights minus a gravimetric quasigeoid model. VCE uncertainty differs for each observation group in the highly unbalanced data, being dependent on the number of observations in each group. It also changes within each group after each VCE iteration, depending on the magnitude of change for each observation group’s variances. It is recommended that VCE uncertainty is computed for VCE updates to the weight matrix for unbalanced data so that the quality of the updates for each group can be properly assessed. This is particularly important if some groups contain relatively small numbers of observations. VCE uncertainty can also be used as a threshold for ceasing iterations, as it is shown—for this data set at least—that it is not necessary to continue time-consuming iterations to fully converge to unity.

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