The Generalized Transmuted Weibull Distribution for Lifetime Data

A new lifetime model, which extends the Weibull distribution using the generalized transmuted-G family proposed by Nofal et al. (2016), called the generalized transmuted Weibull distribution is proposed and studied. Various of its structural properties including ordinary and incomplete moments, quantile and generating functions, probability weighted moments, RØnyi and q-entropies and order statistics are derived. The maximum likelihood method is used to estimate the model parameters. The new distribution is applied to two real data sets to illustrate its exibility. It can serve as an alternative model to other lifetime models available in the literature for modeling positive real data in many areas

New Characterizations of the Pareto Distribution

Characterization results have great importance in statistics and probability applications. Some characterizations of Pareto of the rst kind and Pareto of the second kind distributions are presented by using conditional expectation in terms of their failure (hazard) rate. We also provide two characterization theorems based on the rth truncated moments

The Beta Generalized Inverse Weibull Geometric Distribution

A new six-parameter distribution called the beta generalized inverse Weibull-geometric distribution is proposed. The new distribution is generated from the logit of a beta random variable and includes the generalized inverse Weibull geometric distribution.Various structural properties of the new distribution including explicit expressions for the moments, moment generating function, mean deviation are derived. The estimation of the model parameters is performed by maximum likelihood method

The Transmuted Weibull Lomax Distribution: Properties and Application

A new five parameter model is proposed and stutied. The new distribution generalizes the Weibull Lomax distribution introduced by Tahir et al. (2015) and is referred to as transmuted Weibull Lomax (TWL) distribution. Various structural properties of the new model including ordinary and incomplete moments, quantiles, generating function, probability weighted moments, Rényi and q-entropies and order statistics are derived. We proposed the method of maximum likelihood for estimating the model parameters. The usefulness of the new model is illustrated through an application to a real data set