This paper is an improvement over beta-Nakagami distribution developed by Shittu and Adepoju （2013）. Here we propose the addition of one parameter to the two parameter continuous Nakagami-m distribution （Nakagami, ...This paper is an improvement over beta-Nakagami distribution developed by Shittu and Adepoju （2013）. Here we propose the addition of one parameter to the two parameter continuous Nakagami-m distribution （Nakagami, 1960） that was designed for modeling the fading of radio signals. The resulting distribution referred to as Exponentiated Nakagami （ENAK） distribution is a generalization of the classical Nakagami distribution. The statistical properties of the proposed distribution such as moments, moment generating function, the asymptotic behavior among others were investigated. The method of maximum likelihood is used to estimate the model parameters and the observed information matrix is derived. A real data set is used to compare the new model with the class of Nakagami distributions. Our findings showed that the Exponentiated Nakagami distribution is more flexible than beta-Nakagami distribution with better representation and less computational effort.展开更多
文摘This paper is an improvement over beta-Nakagami distribution developed by Shittu and Adepoju （2013）. Here we propose the addition of one parameter to the two parameter continuous Nakagami-m distribution （Nakagami, 1960） that was designed for modeling the fading of radio signals. The resulting distribution referred to as Exponentiated Nakagami （ENAK） distribution is a generalization of the classical Nakagami distribution. The statistical properties of the proposed distribution such as moments, moment generating function, the asymptotic behavior among others were investigated. The method of maximum likelihood is used to estimate the model parameters and the observed information matrix is derived. A real data set is used to compare the new model with the class of Nakagami distributions. Our findings showed that the Exponentiated Nakagami distribution is more flexible than beta-Nakagami distribution with better representation and less computational effort.
