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A new bivariate exponential distribution for modeling moderately negative dependence

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  • Muhammad Mohsin
  • Hannes Kazianka
  • Jürgen Pilz
  • Albrecht Gebhardt

Abstract

This paper introduces a new bivariate exponential distribution, called the Bivariate Affine-Linear Exponential distribution, to model moderately negative dependent data. The construction and characteristics of the proposed bivariate distribution are presented along with estimation procedures for the model parameters based on maximum likelihood and objective Bayesian analysis. We derive Jeffreys prior and discuss its frequentist properties based on a simulation study and MCMC sampling techniques. A real data set of mercury concentration in largemouth bass from Florida lakes is used to illustrate the methodology. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Muhammad Mohsin & Hannes Kazianka & Jürgen Pilz & Albrecht Gebhardt, 2014. "A new bivariate exponential distribution for modeling moderately negative dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 123-148, March.
    • Handle: RePEc:spr:stmapp:v:23:y:2014:i:1:p:123-148
    DOI: 10.1007/s10260-013-0246-3
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    References listed on IDEAS

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    Cited by:

    1. Manoj Chacko, 2017. "Bayesian estimation based on ranked set sample from Morgenstern type bivariate exponential distribution when ranking is imperfect," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(3), pages 333-349, April.
    2. S. Mirhosseini & M. Amini & D. Kundu & A. Dolati, 2015. "On a new absolutely continuous bivariate generalized exponential distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 61-83, March.

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