The Inverse Weibull Generator of Distributions: Properties and Applications

Abstract

In this paper, we introduce a new family of univariate distributions with two extra positive parameters generated from inverse Weibull random variable called the inverse Weibull generated (IW-G) family. The new family provides a lot of new models as well as contains two new families as special cases. We explore four special models for the new family. Some mathematical properties of the new family including quantile function, ordinary and incomplete moments, probability weighted moments, Rѐnyi entropy and order statistics are derived. The estimation of the model parameters is performed via maximum likelihood method. Applications show that the new family of distributions can provide a better fit than several existing lifetime models.

A New Inverse Pareto Distribution

“…To solve this problem several, introducing several methods including additional one shape parameters, two shape parameters to generating new families of distributions are available in the statistical literature. Some well-known generators are: the generalizedexponential by Gupta and Kundo [8], Kumaraswamy generalized distribution by Cordeiro and de Castro [9], generalized beta-generated by Alexander et al [10], weibull-generated by Bourguignon et al [11], Kumaraswamy weibull-generated by Hassan and Elgarhy [12], generalized additive weibull-generated by Hassan et al [13], inverse weibull-generated Hassan and Nassr [14], odd inverse Pareto-generated by Aldahlan et al [15], modified weibull-generated by Abdelall [16] and more.…”

An Innovative Method for Generating Distributions: Applied to Weibull Distribution

“…We will look at two data sets to describe the significance and flexibility of the MTIW distribution. The first data set was reported by Hassan and Nassr (2018) and is provided in Murthy et al (2004) about time between failures for repairable item. The data are as follows: 1.…”

A New Bivariate Extended Generalized Inverted Kumaraswamy Weibull Distribution

“…The inverse Weibull (IW) distribution is widely used because of its applicability in various fields, like medicine, statistics, engineering, physics, and fluid mechanics [1][2][3][4][5][6][7][8][9][10][11]. To enhance such distributions, researchers introduced new generators by supplementing shape parameters to the base line distribution.…”