JOURNAL ARTICLE

The Compound Family of Generalized Inverse Weibull Power Series Distributions

Amal S. HassanSalwa AssarKareem Ali

Year: 2016 Journal:   British Journal of Applied Science & Technology Vol: 14 (3)Pages: 1-18   Publisher: Sciencedomain International

Abstract

Compounding a continuous lifetime distribution with a discrete one is a useful technique for constructing flexible distributions to facilitate better modeling of lifetime data.In this paper, a new family of lifetime distributions, called the generalized inverse Weibull power series distribution is introduced.This new family is obtained by compounding the generalized inverse Weibull and truncated power series distributions.This compounding procedure follows the same way that was previously carried out by [1].This family contains several new distributions such as generalized inverse Weibull Poisson; inverse Weibull Poisson; inverse Rayleigh Poisson; inverse exponential Poisson; generalized inverse Weibull logarithmic; inverse Weibull logarithmic; inverse Rayleigh logarithmic; inverse exponential logarithmic; generalized inverse Weibull geometric; inverse Weibull geometric; inverse Rayleigh geometric and inverse exponential geometric as special cases.The hazard rate function of the new family of distributions can be increasing, decreasing and bathtub-shaped.Several properties of the new family including; quantile, entropy, moments and distribution of order statistics are provided.The model parameters of the new family are estimated Method Articleby the maximum likelihood method.The two new models namely; generalized inverse Weibull Poisson and the generalized inverse Weibull geometric distributions are studied in some details.Finally, applications to two real data sets are analyzed to illustrate the flexibility and potentiality of the new family.

Keywords:
Weibull distribution Series (stratigraphy) Inverse Mathematics Statistics Econometrics Geology Geometry

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Topics

Statistical Distribution Estimation and Applications
Physical Sciences →  Mathematics →  Statistics and Probability
Mathematical functions and polynomials
Physical Sciences →  Mathematics →  Applied Mathematics
Bayesian Methods and Mixture Models
Physical Sciences →  Computer Science →  Artificial Intelligence
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