| Abstract |
Probability distributions are vital and fundamental to real life and are concerned with creating the basis and foundation for the study of uncertainty. The quality of the procedures used in a statistical analysis depends heavily on the generated distribution. In many applied sciences such as medicine, engineering and finance, among others, modeling and analyzing lifetime data are crucial. This dissertation focuses on developing some new generated families of continuous univariate distributions. Three new families of distributions named the generalized Kumaraswamy-G, generalized odd Lindley-G and odd Lomax-G families are introduced. The new families extend some well-known families in the literature. Their statistical and reliability properties are fully investigated. Several special distributions in the literature are derived from our newly introduced families as special cases. The importance and usefulness of the introduced families are demonstrated in applications by conducting
simulation models and fitting real word data. |
