A study on the estimation of the Transmuted Generalized Uniform Distribution
Résumé
In this paper, we consider the maximum likelihood (ML) estimation of the parameters a new probability distribution recently developed and called transmuted generalized uniform distribution (TGUD). Because of the complicated form of its log-likelihood function, this ML estimation can only be done by using numerical optimization algorithms but this problem has not been studied yet. We address this lack through a comprehensive simulation study in R software using some of the best optimization algorithms (Newton, quasi-Newton and Nelder-Mead algorithms). It is found that the Nelder-Mead algorithm is the best of all the selected algorithms.
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