A theoretical analysis of the odd generalized exponentiated inverse Lomax distribution

Abstract

Various distributions have been proposed to serve as models for wide applications on data from different real-life situations through the extension of existing distribution. This has been achieved in various ways. The Lomax distribution also called “Pareto type II” is a special case of the generalized beta distribution of the second kind,1 and can be seen in many application areas, such as actuarial science, economics, biological sciences, engineering, lifetime and reliability modeling and so on.2 This heavy duty distribution is considered useful as an alternative distribution to survival problems and life-testing in engineering and survival analysis.3 Inverse Lomax distribution is a member of the inverted family of distributions and discovered to be very flexible in analyzing situations with a realized non-monotonic failure rate.4 If a random variable X has a Lomax distribution, then 1 = Y X has an inverse Lomax Distribution. Thus, a random variable X is said to have an Inverted Lomax distribution if the corresponding probability density function and cumulative density function are given by Yadav et al.5