Open Access Open Access  Restricted Access Subscription or Fee Access

On the maximum likelihood method for the transmuted exponentiated gamma distribution

Issa Cherif Geraldo


The last two decades have seen the development and the popularization of new families of distributions in order to improve data fitting. Because of the complex forms of the probability density functions of these new distributions, the estimation of parameters can only be done by using numerical optimization algorithms but, in many papers, this numerical optimization problem is not studied in depth and the choice of the optimization algorithm is simply neglected. In this paper, we study the disturbing example of the Transmuted exponentiated gamma (TEG) distribution, an important distribution in lifetime tests, for which estimates depend on the selected optimization algorithms. Our aim is to show through the example of the TEG distribution, that, to implement the maximum likelihood method for a distribution, it is necessary to compare several optimization algorithms in order to determine the most effective one before making applications to real data.


Numerical optimization, iterative method, maximum likelihood, exponentiated gamma.

Full Text:


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.