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Semi-parametric multivariate modelling when the marginals are the same

Author

Listed:
  • Marron, J. S.
  • Nakamura, Miguel
  • Pérez-Abreu, Víctor

Abstract

A model is developed for multivariate distributions which have nearly the same marginals, up to shift and scale. This model, based on "interpolation" of characteristic functions, gives a new notion of "correlation". It allows straightforward nonparametric estimation of the common marginal distribution, which avoids the "curse of dimensionality" present when nonparametically estimating the full multivariate distribution. The method is illustrated with environmental monitoring network data, where multivariate modelling with common marginals is often appropriate.

Suggested Citation

  • Marron, J. S. & Nakamura, Miguel & Pérez-Abreu, Víctor, 2003. "Semi-parametric multivariate modelling when the marginals are the same," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 310-329, August.
    • Handle: RePEc:eee:jmvana:v:86:y:2003:i:2:p:310-329
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    References listed on IDEAS

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    1. Caselton, W. F. & Zidek, J. V., 1984. "Optimal monitoring network designs," Statistics & Probability Letters, Elsevier, vol. 2(4), pages 223-227, August.
    2. Samorodnitsky, Gennady, 1995. "Association of infinitely divisible random vectors," Stochastic Processes and their Applications, Elsevier, vol. 55(1), pages 45-55, January.
    3. Sato, Ken-iti, 1982. "Absolute continuity of multivariate distributions of class L," Journal of Multivariate Analysis, Elsevier, vol. 12(1), pages 89-94, March.
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