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Prediction of Euclidean distances with discrete and continuous outcomes


Author Info

  • Mortier, F.
  • Robin, S.
  • Lassalvy, S.
  • Baril, C.P.
  • Bar-Hen, A.
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    The objective of this paper is first to predict generalized Euclidean distances in the context of discrete and quantitative variables and then to derive their statistical properties. We first consider the simultaneous modelling of discrete and continuous random variables with covariates and obtain the likelihood. We derive an important property useful for its practical maximization. We then study the prediction of any Euclidean distances and its statistical proprieties, especially for the Mahalanobis distance. The quality of distance estimation is analyzed through simulations. This results are applied to our motivating example: the official distinction procedure of rapeseed varieties.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 97 (2006)
    Issue (Month): 8 (September)
    Pages: 1799-1814

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    Handle: RePEc:eee:jmvana:v:97:y:2006:i:8:p:1799-1814

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    Keywords: Multivariate probit model Euclidean distance Mahalanobis distance Varietal distinctness;


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    Cited by:
    1. Chaubert, F. & Mortier, F. & Saint André, L., 2008. "Multivariate dynamic model for ordinal outcomes," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1717-1732, September.


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