The Bivariate Erlang and its Application in Modeling Recurrence Times of Kidney Dialysis Data


  • Norou Diawara Old Dominion University, 4700 Elkhorn Ave., Norfolk, VA 23529, USA
  • S.H. Sathish Indika Thomas Nelson Community College, 99 Thomas Nelson Drive, Hampton, VA 23666, USA
  • Melva Grant Old Dominion University, 4700 Elkhorn Ave., Norfolk, VA 23529, USA
  • Edgard M. Maboudou-Tchao University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816, USA



Bivariate models, Erlang, exponential, Dirac delta


Recent advances in computer modeling allows us to find closer fits to data. Our emphasis is on the interdependence between occurrence at kidney dialysis. The interdependence between kidney dialysis occurrences is modelled by a bivariate exponential that we propose in this article. The application is shown on the McGilchrist and Aisbett kidney data set with the use of the exponential distribution. The proposed bivariate exponential model has exponential marginal densities, correlated via a latent random variables and with finite probability of simultaneous occurrence. Extension of the model to a bivariate Erlang type distribution with same shape parameter is presented.


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How to Cite

Diawara, N., Indika, S. S., Grant, M., & Maboudou-Tchao, E. M. (2014). The Bivariate Erlang and its Application in Modeling Recurrence Times of Kidney Dialysis Data. International Journal of Statistics in Medical Research, 3(2), 88–93.



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