No abstract

Statistical distributions are important in modeling the real life of an item and therefore proper distributions that provide useful information for sound conclusions and decisions are needed. For that reason, the demand for developing new generalized distributions have become appropriate for data that have both monotonic and non-monotonic hazard rate functions. In this paper, we develop a new distribution called the Type II Topp-Leone-G Power Series (TIITLGPS) distribution by compounding the Type II Topp-Leone-G (TIITLG) distribution with the power series distribution. Statistical properties of the TIITLGPS distribution are obtained. A variety of shapes for the densities and hazard rate are presented of the considered special case. A simulation study to examine the efficiency of the maximum likelihood estimates is also conducted. Finally, the bladder cancer data example is analyzed for illustrative purposes, it is displayed that the introduced distribution provides better fit when compared to other non-nested distributions considered in this work.

We introduce a new family of distributions, referred to as the Gompertz-Topp-Leone-G (Gom-TL-G) distribution. The new family of distributions can be expressed as an infinite linear combination of the Exp-G distributions. It can handle extremely tailed data and has both monotonic and non-monotonic hazard rate functions. A simulation study is used to assess the estimation method. Some real data examples are analyzed for illustrative purposes.

A new generalized class of distributions called the Exponentiated Half Logistic-Generalized G Power Series (EHL-GGPS) distribution is proposed. We present some special cases in the proposed distribution. Several mathematical properties of the EHL-GGPS distribution were also derived including order statistics, moments and maximum likelihood estimates. A simulation study for selected parameter values is presented to examine the consistency of the maximum likelihood estimates. Finally, some real data applications of the EHL-GGPS distribution are presented to illustrate the usefulness of the proposed class of distributions.