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Discussion on "Spatial prediction in the presence of positional error"

Journal Article


Abstract


  • In the quantitative Geography literature, particularly in Geographical Information Sciences, the concepts of location error and attribute

    error are firmly established. What to do about these sources of error has often been handled separately and not always statistically. In the

    geostatistics literature, attribute error is referred to as “regional variability” or “spatial variability,” and it is often assumed that there is no

    location error. The article by Fanshawe and Diggle (hereafter, FD) builds on earlier work by Cressie and others on spatial statistical methods

    where both spatial variability and location (equivalently, positional) error is modeled. The authors extend that approach, which was based on

    empirical hierarchical modeling, to Bayesian hierarchical modeling.

    Suppose Y are data, S is the process (of possibly different dimensions than Y), and θ are the parameters. The Bayesian hierarchical model

    (BHM) models the joint distribution

Publication Date


  • 2011

Citation


  • Cressie, N. A. (2011). Discussion on "Spatial prediction in the presence of positional error". Environmetrics, 22 (2), 125-126.

Scopus Eid


  • 2-s2.0-79953154608

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2275

Number Of Pages


  • 1

Start Page


  • 125

End Page


  • 126

Volume


  • 22

Issue


  • 2

Abstract


  • In the quantitative Geography literature, particularly in Geographical Information Sciences, the concepts of location error and attribute

    error are firmly established. What to do about these sources of error has often been handled separately and not always statistically. In the

    geostatistics literature, attribute error is referred to as “regional variability” or “spatial variability,” and it is often assumed that there is no

    location error. The article by Fanshawe and Diggle (hereafter, FD) builds on earlier work by Cressie and others on spatial statistical methods

    where both spatial variability and location (equivalently, positional) error is modeled. The authors extend that approach, which was based on

    empirical hierarchical modeling, to Bayesian hierarchical modeling.

    Suppose Y are data, S is the process (of possibly different dimensions than Y), and θ are the parameters. The Bayesian hierarchical model

    (BHM) models the joint distribution

Publication Date


  • 2011

Citation


  • Cressie, N. A. (2011). Discussion on "Spatial prediction in the presence of positional error". Environmetrics, 22 (2), 125-126.

Scopus Eid


  • 2-s2.0-79953154608

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2275

Number Of Pages


  • 1

Start Page


  • 125

End Page


  • 126

Volume


  • 22

Issue


  • 2