On Concomitants of Dual Generalized Order Statistics for a Bivariate Inverse Exponential Distribution

Authors

  • Saman Hanif Shahbaz
  • Muhammad Qaiser Shahbaz Department of Statistics, Faculty of Sciences, King Abdul Aziz University, Jeddah, Saudi Arabia

DOI:

https://doi.org/10.29020/nybg.ejpam.v11i4.3257

Keywords:

Concomitants, Dual Generalized Order Statistics, Bivariate Inverse Exponential distribution.

Abstract

The concomitants of Dual Generalized Order Statistics for Inverse
Exponential distribution has been studied. Specifically the distributional
properties of r--th concomitant and joint distribution of r--th and s--th
concomitant of dual generalized order statistics have been studied when
sample is available from a bivariate inverse exponential distribution.

Downloads

Published

2018-10-24

Issue

Vol. 11 No. 4: (October 2018)

Section

Econometrics and Statistics

License

Upon acceptance of an article by the European Journal of Pure and Applied Mathematics, the author(s) retain the copyright to the article. However, by submitting your work, you agree that the article will be published under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license allows others to copy, distribute, and adapt your work, provided proper attribution is given to the original author(s) and source. However, the work cannot be used for commercial purposes.

By agreeing to this statement, you acknowledge that:

  • You retain full copyright over your work.
  • The European Journal of Pure and Applied Mathematics will publish your work under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
  • This license allows others to use and share your work for non-commercial purposes, provided they give appropriate credit to the original author(s) and source.

How to Cite

On Concomitants of Dual Generalized Order Statistics for a Bivariate Inverse Exponential Distribution. (2018). European Journal of Pure and Applied Mathematics, 11(4), 929-936. https://doi.org/10.29020/nybg.ejpam.v11i4.3257