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Encyclopedia of Geodesy pp 1–5Cite as

Best Integer Prediction in Mixed Models

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Definitions

Best predictor : Predictor having the smallest mean square prediction error (best) of all predictors within a certain class.

Mixed integer model : Model of observation equations having both real-valued and integer-valued unknown parameters.

Introduction

The prediction of spatially and/or temporally varying variates finds its application in various spatial and Earth science disciplines. In physical geodesy, where it is used with a trend-signal-noise model to predict functionals of the gravity field, it is known as least-squares collocation (Krarup, 1969; Moritz, 1978; Sanso and Tscherning, 2003). The trend-signal-noise model also forms the basis of prediction in geostatistics, where optimal linear prediction is called Kriging, named after Krige (1951) and further developed by Matheron (1970). When the trend is unknown, it is referred to as universal Kriging, and when the trend is absent or set to zero, it is called simple Kriging (Herzfeld, 1992; Wackernagel, 1995; Olea, 1999...

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References and Reading

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Author information

Authors and Affiliations

  1. GNSS Research Centre, Department of Spatial Sciences, Curtin University of Technology, Perth, Australia

    P. J. G. Teunissen

  2. Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands

    P. J. G. Teunissen

Authors
  1. P. J. G. Teunissen
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Correspondence to P. J. G. Teunissen .

Editor information

Editors and Affiliations

  1. Geodetic Institute, University of Stuttgart, Stuttgart, Germany

    Erik Grafarend

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© 2015 Springer International Publishing Switzerland

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Teunissen, P.J.G. (2015). Best Integer Prediction in Mixed Models. In: Grafarend, E. (eds) Encyclopedia of Geodesy. Springer, Cham. https://doi.org/10.1007/978-3-319-02370-0_3-1

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  • DOI: https://doi.org/10.1007/978-3-319-02370-0_3-1

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  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-02370-0

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

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