Because of their flexibility, recently, much attention has been given to the study of generalized distributions. A complete study of the beta generalized logistic distribution (type IV) is proposed, introducing an approximate form for the median and deducing the mean deviation from the mean and the median. A complete parameter estimation using the method of maximum likelihood and the method of moments is presented. Some characteristic properties of the generalized logistic distribution type I are discussed. Also, a highlight to some properties of an analog distribution to the generalized logistic distribution type IV, discussed by Zografos and Balakrishnan [1], is presented.

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This paper introduces a new distribution named Exponential Modified Weibull logistic distribution. This distribution generalizes the following distributions: (1) Linear Failure Rate Logistic Distribution, (2) Weibull Logistic Distribution, (3) Rayleigh Logistic Distribution, (4) Exponential Logistic Distribution, where the failure rate, Weibull, Rayleigh and exponential distributions are the distributions most used for analyzing lifetime data. The properties of the new distribution are derived that include expressions for the rthmoment, characteristic function and quantile function. The estimation of model parameters are performed by the method of maximum likelihood and hence evaluation of the performance of maximum likelihood estimation using simulation.

Because of their flexibility, recently, much attention has been given to the study of generalized distributions. A complete study of the transmuted Kumaraswamy Logistic distribution is proposed, introducing some basic properties of this distribution, such as quantile function, characteristic function and entropy are derived, as well as the derivation of maximum likelihood estimates of the parameters and the information matrix, Real life data is used as an application to this distribution with a comparison with other distributions to illustrate the flexibility and ability to model lifetime data.   Also, a simulation study is conducted to demonstrate the effect of the sample on the estimates of the parameters.