In title page Institute of Statistical Studies and Research (I.S.S.R)

Thesis (Ph.D.) - Cairo University - Faculity of Graduat Studies of Statistical Research - Department of Mathematical Statistics

The quality of the procedures used in a statistical analysis depends heavily on the assumed probability model or distributions. Because of this, considerable efforts have been expended in the development of large classes of standard probability distributions along with relevant statistical methodologies by adding new parameters to expanding classical distributions in order to obtain more flexibility. So, in recent years there has been an increased interest in defining new generators for univariate continuous distributions by introducing one or more additional parameter(s) to the baseline distribution. This induction of parameter(s) has been proved useful in exploring tail properties and also for improving the goodness-of-fit of the proposed generated family and provides great flexibility in modeling data in practices This has motivated the author to introduce a new model and a new generator. The new model is derived on the basis of compounding technique and shows its flexibility where several other distributions follow as special cases by selecting the appropriate values of the parameters. To test the newly produced model in fitting different data, the new model along with other competitive models are fitted to real data sets; the proposed model has shown great performance

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