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In this paper, we introduce a new class of distributions called the Burr X family. Some of its mathematical and structural properties are derived. The maximum likelihood is used for estimating the model parameters. The importance and flexibility of the new family are illustrated by means of an application to real data set.

We introduce a new class of continuous distributions called the transmuted exponentiated generalized-G family which extends the exponentiated generalized-G class introduced by Cordeiro et al. (2013). We provide some special models for the new family. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Rényi and Shannon entropies, order statistics and probability weighted moments are derived. The estimation of the model parameters is performed by maximum likelihood. The flexibility of the proposed family is illustrated by means of an application to a real dataset.

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