On Bivariate Nadarajah-Haghighi Distribution derived from Farlie-Gumbel-Morgenstern copula in the Presence of Covariates

https://doi.org/10.46481/jnsps.2023.871

Authors

  • Yakubu Aliyu Department of Statistics, Ahmadu Bello University Zaria-Nigeria
  • Umar Usman Department of Mathematics, Usmanu Danfodiyo University Sokoto-Nigeria

Keywords:

Exponential Distribution, Nadarajah-Haghighi Distribution, Bivariate Models and Copula Function.

Abstract

An important alternative distribution to the Weibull, generalized exponen-
tial and gamma distributions that is used in survival analysis is the Nadarajah-
Haghighi exponential distribution. Similar to the Weibull, generalized exponen-
tial and gamma distributions, the Nadarajah-Haghighi exponential distribution
is an extension of the well known exponential distribution. In this paper, a copula
function commonly used to model very weak linear dependence was used to intro-
duced a bivariate Nadarajah-Haghighi distribution. The joint survival function,
joint probability density function and joint cumulative distribution were given
in closed form. Bayesian method of estimation was used to estimate the model
parameters considering the presence of right censoring and covariates. Posterior
summaries of interest were obtained via standard Markov Monte Carlo (MCMC )
technique. Two real data sets were used to illustrate the importance and flexi-
bility of the bivariate model in comparison with some competing models. It was
observed that, the bivariate Nadarajah-Haghighi distribution provides a better flt
than bivariate exponential, bivariate Weibull, bivariate generalized exponential
and bivariate modified Weibull distributions.

Dimensions

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Published

2023-05-01

How to Cite

Aliyu, Y., & Usman, U. (2023). On Bivariate Nadarajah-Haghighi Distribution derived from Farlie-Gumbel-Morgenstern copula in the Presence of Covariates. Journal of the Nigerian Society of Physical Sciences, 5(2), 871. https://doi.org/10.46481/jnsps.2023.871

Issue

Volume 5, Issue 2, May 2023 (In Progress)

Section

Original Research
Copyright & Licensing

Copyright (c) 2023 Yakubu Aliyu, Umar Usman

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