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In this paper, we introduce and study a new family of continuous distributions called Kumaraswamy Weibull-generated () G KwWfamily of distributions which is an extension of the Weibull-G family of distributions proposed by Bourguignon in [3]. The new family includes several known models. We obtain general explicit expressions for the quantile function, moments, probability weighted moments, generating function, mean deviation and order statistics. Making use of maximum likelihood method, we discuss the model Amal S. Hassan and M. Elgarhy 206 parameters. Furthermore, besides showing the usefulness of our proposed family of distributions by considering a three real data set, evidence of this family to outperform other classes of lifetime models has been noticed.

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In this paper, a three-parameter lifetime model motivated by alpha power transformation is considered. We call the proposed distribution as; the alpha power transformed extended exponential (APTEE). The APTEE model contains new recent models as; alpha power transformed exponential and alpha power transformed Lindley distributions. At the same time, it contains classical models as exponential, gamma, and Lindley distributions. The properties of the APTEE distribution are derived. Parameter estimation is accomplished using maximum likelihood, percentiles, and Cramer-von Mises methods. Simulation issues and applications to real data are emphasized.

In this paper, we introduce a new generalization of the power Lindley distribution referred to as the alpha power transformed power Lindley (APTPL). The APTPL model provides a better fit than the power Lindley distribution. It includes the alpha power transformed Lindley, power Lindley, Lindley, and gamma as special submodels. Various properties of the APTPL distribution including moments, incomplete moments, quantiles, entropy, and stochastic ordering are obtained. Maximum likelihood, maximum products of spacings, and ordinary and weighted least squares methods of estimation are utilized to obtain the estimators of the population parameters. Extensive numerical simulation is performed to examine and compare the performance of different estimates. Two important data sets are employed to show how the proposed model works in practice.

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In this paper, we present a new family, depending on additive Weibull random variable as a generator, called the generalized additive Weibull generated-family (GAW-G) of distributions with two extra parameters. The proposed family involves several of the most famous classical distributions as well as the new generalized Weibull-G family which already accomplished by Cordeiro et al. (2015). Four special models are displayed. The expressions for the incomplete and ordinary moments, quantile, order statistics, mean deviations, Lorenz and Benferroni curves are derived. Maximum likelihood method of estimation is employed to obtain the parameter estimates of the family. The simulation study of the new models is conducted. The efficiency and importance of the new generated family is examined through real data sets.

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