Unification of the recently introduced Kumaraswamy Marshall-Olkin-G and Beta Marshall-Olkin-G family of distributions is proposed. A number of important statistical and mathematical properties of the family is investigated. A distribution belonging to the proposed family is shown to perform better than the corresponding distribution from the Kumaraswamy Marshall-Olkin-G and Beta Marshall-Olkin-G family of distributions by considering data fitting with three real life data sets.

A new family of continuous probability distributions is proposed by using Kumaraswamy-G distribution as the base line distribution in the Marshall-Olkin construction. A number of known distributions are derived as particular cases. Various properties of the proposed family like formulation of the pdf as different mixture of exponentiated baseline distributions, order statistics, moments, moment generating function, Rényi entropy, quantile function and random sample generation have been investigated. Asymptotes, shapes and stochastic ordering are also investigated. Characterizations of the proposed family based on truncated moments, hazard function and reverse hazard function are also presented. The parameter estimation by method of maximum likelihood, their large sample standard errors and confidence intervals and method of moment are also discussed. Two members of the proposed family are compared with different sub models and also with the corresponding members of KumaraswamyMarshall-Olkin-G family by fitting of two real life data sets.

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We study a new family of distributions defined by the minimum of the Poisson random number of independent and identically distributed random variables having the Topp Leone-G distribution. Some mathematical properties of the new family are derived. Maximum likelihood estimation of the model parameters is investigated. Two special models of the new family are discussed. We perform three applications to real data sets to show the potentiality of the proposed family. In order to test the validity of the new family, a modified Chi-squared goodness-of-fit test based on Nikulin-Rao-Robson statistics is proposed theoretically.

We proposed a so-called Beta Kumaraswamy Burr Type X distribution which gives the extension of the Kumaraswamy-G class of family distribution. Some properties of this proposed model were provided, like: the expansion of densities and quantile function. We considered the Bayes and maximum likelihood methods to estimate the parameters and also simulate the model parameters to validate the methods based on different set of true values. Some real data sets were employed to show the usefulness and flexibility of the model which serves as generalization to many sub-models in the fields of engineering, medical, survival and reliability analysis.