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In this paper, we propose a new class of distributions called the Lindley generator with one extra parameter to generate many continuous distributions. The new distribution contains several distributions as submodels, such as Lindley-Exponential, Lindley-Weibull, and LindleyLomax. Some mathematical properties of the new generator, including ordinary moments, quantile and generating functions, limiting behaviors, some entropy measures and order statistics, which hold for any baseline model, are presented. Then, we discuss the maximum likelihood method to estimate model parameters. The importance of the new generator is illustrated by means of three real data sets. Applications show that the new family of distributions can provide a better fit than several existing lifetime models.

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We introduce a new three-parameter lifetime model called the Lindley Weibull distribution, which accommodates unimodal and bathtub, and a broad variety of monotone failure rates. We provide a comprehensive account of some of its mathematical properties including ordinary and incomplete moments, quantile and generating functions and order statistics. The new density function can be expressed as a linear combination of exponentiated Weibull densities. The maximum likelihood method is used to estimate the model parameters. We present simulation results to assess the performance of the maximum likelihood estimation. We prove empirically the importance and flexibility of the new distribution in modeling two data sets.

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We introduce a new family of continuous distributions called the complementary geometric transmuted-G family, which extends the transmuted family proposed by Shaw and Buckley (2007). Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, quantile and generating functions, entropies, order statistics and probability weighted moments are derived. Two special models of the introduced family are discussed in detail. The maximum likelihood method is used for estimating the model parameters. The importance and flexibility of the new family are illustrated by means of two applications to real data sets. We provide some simulation results to assess the performance of the proposed model.