Abstract:
The development of generalized classes of distributions have attracted the attention of
both theoretical and applied statisticians in recent times due to their
exible statistical
properties. In this study, the exponentiated generalized transformed-transformer family
of distributions was proposed and studied. The statistical properties of the new family
were derived and various sub-families were de ned. Some of the sub-families were used
to develop the exponentiated generalized exponential Dagum, new generalized modi ed
inverse Rayleigh and exponentiated generalized half logistic Burr X distributions. The
statistical properties of the proposed distributions were studied and inferences were made
on them. An extension of a sub-family of the exponentiated generalized transformed-
transformer was developed by compounding it with the power series class to obtain the
exponentiated generalized power series family of distributions. Monte Carlo simulations
were performed to investigate the properties of the maximum likelihood estimators for
the parameters of the developed distributions. The results revealed that the maximum
likelihood estimators for the parameters were consistent. Applications of the proposed
distributions were demonstrated using real data sets and their performance were com-
pared to other known competing models. The proposed distributions showed greater
exibility and can be used to model di erent kinds of real data sets.
