Skip to main content
Log in

Farlie-Gumbel-Morgenstern bivariate densities: Are they applicable in hydrology?

  • Originals
  • Published:
Stochastic Hydrology and Hydraulics Aims and scope Submit manuscript

Abstract

Certain bivariate densities constructed from marginals have recently been suggested as models of hydrologic variates such as rainfall intensity and depth. It is pointed out that (i) these densities belong to the families of the Farlie-Gumbel-Morgenstern densities and the Farlie polynomial densities, which have been extensively studied in the statistical literature, and that (ii) these densities have a limited potential applicability in hydrology since they can model only weakly associated variates, whose product-moment correlationR is within the range |R|≤1/3, under the first family of densities, and |R|≤1/2 under the second family.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Choulakian, V.; El-Jabi, N.; Moussi, J. 1990: On the distribution of flood volume in partial duration series analysis of flood phenomena. Stochastic Hydrology and Hydraulics 4, 217–226

    Google Scholar 

  • Farlie, D. J. G. 1960: The performance of some correlation coefficients for a general bivariate distribution. Biometrika 47 (3), 307–323

    Google Scholar 

  • Gumbel, E. J. 1958: Distributions á plusieurs variables dont les marges sont données (with remarks by Fréchet, M.). Comptes Rendus de l'Académie des Science, Paris, 246, 2717–2720

    Google Scholar 

  • Gumbel, E. J. 1960a: Bivariate exponential distributions. Journal of the American Statistical Association 55, 698–707

    Google Scholar 

  • Gumbel, E. J. 1960b: Multivariate distributions with given margins and analytical examples. Bulletin de l'Institut International de Statistique, Bruxelless 37 (3), 1–13

    Google Scholar 

  • Haight, F. A. 1961: Index to the distributions of mathematical statistics. Journal of Research of the National Bureau of Standards 65B (1), 23–60

    Google Scholar 

  • Kotz, S. 1975: Multivariate distributions at a cross road. In Patil, G. P.; Kotz, S.; Ord, J. K. (eds) Statistical distributions in scientific work 1, pp. 247–270, D. Reidel Publishing Company, Dordrecht, Holland

    Google Scholar 

  • Kotz, S.; Johnson, N. 1977: Propriétés de dépendance des distributions itérées, généralisées á deux variables Farlie-Gumbel-Morgenstern. Comptes Rendus de l'Académie des Science, Paris, 285 (4), 277–280

    Google Scholar 

  • Long, D.; Krzysztofowicz, R. 1991: A family of multivariate densities constructed from marginals. Working Paper, Department of Systems Engineering, University of Virginia, Charlottesville, June

    Google Scholar 

  • Mardia, K. V. 1970: Families of bivariate distributions. Hafner Publishing Company, Darien, Connecticut

    Google Scholar 

  • Marshall, A. W.; Olkin, I. 1967: A multivariate exponential distribution. Journal of the American Statistical Association 62, 30–44

    Google Scholar 

  • Marshall, A. W.; Olkin, I. 1988: Families of multivariate distributions. Journal of the American Statistical Association 83 (403), 834–841

    Google Scholar 

  • Morgenstern, D. 1956: Einfache beispiele zweidimensionaler verteilungen. Mitteilungeblatt für mathematische statistik, Würzburg, 8 (3), 234–235

    Google Scholar 

  • Plackett, R. L. 1965: A class of bivariate distributions. Journal of the American Statistical Association 60, 516–522

    Google Scholar 

  • Rosbjerg, D. 1987: On the annual maximum distribution in dependent partial duration series. Stochastic Hydrology and Hydraulics 1, 3–16

    Google Scholar 

  • Schucany, W. R.; Parr, W. C.; Boyer, J. E. 1978: Correlation structure in Farlie-Gumbel-Morgenstern distributions. Biometrika 65 (3), 650–653

    Google Scholar 

  • Singh, K.; Singh, V. P. 1991: Derivation of bivariate probability density functions with exponential marginals. Stochastic Hydrology and Hydraulics 5 (1), 55–68

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Long, D., Krzysztofowicz, R. Farlie-Gumbel-Morgenstern bivariate densities: Are they applicable in hydrology?. Stochastic Hydrol Hydraul 6, 47–54 (1992). https://doi.org/10.1007/BF01581674

Download citation

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01581674

Key words

Navigation