Bounded M-O Extended Exponential Distribution with Applications

Indranil Ghoshhttp://orcid.org/https://orcid.org/0000-0001-7910-8919 1 , Sanku Dey 2 ,  and Devendra Kumar 3
  • 1 Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, USA
  • 2 Department of Statistics, St. Anthony’s College, 793001, Meghalaya, Shillong, India
  • 3 Department of Statistics, Central University of Haryana, Haryana, India
Indranil GhoshORCID iD: https://orcid.org/0000-0001-7910-8919, Sanku Dey and Devendra Kumar

Abstract

In this paper a new probability density function with bounded domain is presented. This distribution arises from the Marshall–Olkin extended exponential distribution proposed by Marshall and Olkin (1997). It depends on two parameters and can be considered as an alternative to the classical beta and Kumaraswamy distributions. It presents the advantage of not including any additional parameter(s) or special function in its formulation. The new transformed model, called the unit-Marshall–Olkin extended exponential (UMOEE) distribution which exhibits decreasing, increasing and then bathtub shaped density while the hazard rate has increasing and bathtub shaped. Various properties of the distribution (including quantiles, ordinary moments, incomplete moments, conditional moments, moment generating function, conditional moment generating function, hazard rate function, mean residual lifetime, Rényi and δ-entropies, stress-strength reliability, order statistics and distributions of sums, difference, products and ratios) are derived. The method of maximum likelihood is used to estimate the model parameters. A simulation study is carried out to examine the bias, mean squared error and 95  asymptotic confidence intervals of the maximum likelihood estimators of the parameters. Finally, the potentiality of the model is studied using two real data sets. Further, a bivariate extension based on copula concept of the proposed model are developed and some properties of the distribution are derived. The paper is motivated by two applications to real data sets and we hope that this model will be able to attract wider applicability in survival and reliability.

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