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Discriminant Analysis to Test Non-Nested Hypotheses

  • Conference paper
Geodetic Theory Today

Part of the book series: International Association of Geodesy Symposia ((IAG SYMPOSIA,volume 114))

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Summary

The comparison of different (linear) models, representing different geodetic/geophysic hypotheses, on the light of observational data leads to a testing procedure based on residuals between data and manifolds in general different positions. This topic has already been mentioned in statistical literature as comparison of non-nested linear models. The problem is here defined and completely solved by a Bayesian approach.

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References

  • Backus G.L. (1971). Inference from inadequate and inaccurate data, Lectures in Applied Mathematics n. 14, pp. 1–105.

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Author information

Authors and Affiliations

  1. Dipartimento di Georisorse e Territorio, Politecnico di Torino, Turin, Italy

    B. Betti

  2. Dipartimento di Idraulica, Trasporti e Strade, Universitá di Roma ”La Sapienza”, Rome, Italy

    M. Crespi

  3. Dipartimento di Ingegneria Idraulica, Ambientale e del Rilevamento, Politecnico di Milano, Milan, Italy

    F. Sansó

  4. Dipartimento di Ingegneria Civile ed Ambientale, Universitá di Trento, Trento, Italy

    D. Sguerso

Authors
  1. B. Betti
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  2. M. Crespi
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  3. F. Sansó
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  4. D. Sguerso
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Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria Idraulica, Ambientale e del Rilevamento, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milano, Italy

    Fernando Sansò

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© 1995 Springer-Verlag Berlin Heidelberg

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Betti, B., Crespi, M., Sansó, F., Sguerso, D. (1995). Discriminant Analysis to Test Non-Nested Hypotheses. In: Sansò, F. (eds) Geodetic Theory Today. International Association of Geodesy Symposia, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79824-5_34

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  • DOI: https://doi.org/10.1007/978-3-642-79824-5_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59421-5

  • Online ISBN: 978-3-642-79824-5

  • eBook Packages: Springer Book Archive

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