On the Properties and Applications of a Transmuted Lindley-Exponential Distribution

Adamu Abubakar Umar *

School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia.

Innocent Boyle Eraikhuemen

Department of Mathematics and Computer Science, Benson Idahosa University, Benin City, Nigeria.

Peter Oluwaseun Koleoso

Department of Statistics, University of Ibadan, Ibadan, Oyo State, Nigeria.

Jerry Joel

Department of Statistics and Operations Research, MAUTech, P.M.B. 2076, Yola, Nigeria.

Terna Godfrey Ieren

Department of Statistics and Operations Research, MAUTech, P.M.B. 2076, Yola, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

The Quadratic rank transmutation map proposed for introducing skewness and flexibility into probability models with a single parameter known as the transmuted parameter has been used by several authors and is proven to be useful. This article uses this method to add flexibility to the Lindley-Exponential distribution which results to a new continuous distribution called “transmuted Lindley-Exponential distribution”. This paper presents the definition, validation, properties, application and estimation of unknown parameters of the transmuted Lindley-Exponential distribution using the method of maximum likelihood estimation. The new distribution has been applied to a real life dataset on the survival times (in days) of 72 guinea pigs and the result gives good evidence that the transmuted Lindley-Exponential distribution is better than the Lindley-Exponential distribution, Exponential distribution and Lindley distribution based on the dataset used.

Keywords: Quadratic rank transmutation map, Transmuted Lindley-Exponential distribution, definition, properties, maximum likelihood estimation, applications.


How to Cite

Umar, Adamu Abubakar, Innocent Boyle Eraikhuemen, Peter Oluwaseun Koleoso, Jerry Joel, and Terna Godfrey Ieren. 2019. “On the Properties and Applications of a Transmuted Lindley-Exponential Distribution”. Asian Journal of Probability and Statistics 5 (3):1-13. https://doi.org/10.9734/ajpas/2019/v5i330139.

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