Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    On a stronger-than-best property for best prediction

    186104_186104.pdf (155.2Kb)
    Access Status
    Open access
    Authors
    Teunissen, Peter
    Date
    2008
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Teunissen, P.J.G. 2008. On a stronger-than-best property for best prediction. Journal of Geodesy. 82 (3): pp. 167-175.
    Source Title
    Journal of Geodesy
    DOI
    10.1007/s00190-007-0169-6
    ISSN
    09497714
    URI
    http://hdl.handle.net/20.500.11937/34386
    Collection
    • Curtin Research Publications
    Abstract

    The minimum mean squared error (MMSE) criterion is a popular criterion for devising best predictors. In case of linear predictors, it has the advantage that no further distributional assumptions need to be made, other then about the first- and second-order moments. In the spatial and Earth sciences, it is the best linear unbiased predictor (BLUP) that is used most often. Despite the fact that in this case only the first- and second-order moments need to be known, one often still makes statements about the complete distribution, in particular when statistical testing is involved. For such cases, one can do better than the BLUP, as shown in Teunissen (J Geod. doi: 10.1007/s00190-007-0140-6, 2006), and thus devise predictors that have a smaller MMSE than the BLUP. Hence, these predictors are to be preferred over the BLUP, if one really values the MMSE-criterion. In the present contribution, we will show, however, that the BLUP has another optimality property than the MMSE-property, provided that the distribution is Gaussian. It will be shown that in the Gaussian case, the prediction error of the BLUP has the highest possible probability of all linear unbiased predictors of being bounded in the weighted squared norm sense. This is a stronger property than the often advertised MMSE-property of the BLUP.

    Related items

    Showing items related by title, author, creator and subject.

    • Best prediction in linear models with mixed integer/real unknowns: theory and application
      Teunissen, Peter (2007)
      In this contribution, we extend the existing theory of minimum mean squared error prediction (best prediction). This extention is motivated by the desire to be able to deal with models in which the parameter vectors have ...
    • Equalisation for carrierless amplitude and phase modulation
      Gao, Jason (2002)
      Carrierless amplitude and phase (CAP) modulation is generally regarded as a bandwidth efficient two-dimensional (2-D) passband line code. It is closely related to the pulse amplitude modulation (PAM) and quadrature amplitude ...
    • Optimum use of the flexible pavement condition indicators in pavement management system
      Shiyab, Adnan M S H (2007)
      This study aimed at investigating the current practices and methods adopted by roads agencies around the world with regard to collection, analysis and utilization of the data elements pertaining to the main pavement ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.