2016
DOI: 10.1155/2016/7581918
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Classical and Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution

Abstract: Nakagami distribution is considered. The classical maximum likelihood estimator has been obtained. Bayesian method of estimation is employed in order to estimate the scale parameter of Nakagami distribution by using Jeffreys’, Extension of Jeffreys’, and Quasi priors under three different loss functions. Also the simulation study is conducted in R software.

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Cited by 19 publications
(22 citation statements)
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“…The rate parameter of Erlang distribution is estimated with the help of different loss functions. For parameter estimation we have used the approach as is used by Ahmad et al [34], Ahmad et al [35], etc. Some important prior distributions and loss functions which we have used in this article are given as below:…”
Section: Bayesian Methods Of Estimationmentioning
confidence: 99%
“…The rate parameter of Erlang distribution is estimated with the help of different loss functions. For parameter estimation we have used the approach as is used by Ahmad et al [34], Ahmad et al [35], etc. Some important prior distributions and loss functions which we have used in this article are given as below:…”
Section: Bayesian Methods Of Estimationmentioning
confidence: 99%
“…These assumed prior distributions have been used widely by several authors including [22][23][24][25][26][27][28][29]. This study also considers three loss functions including square error, quadratic and precautionary loss functions which have also been used previously by some researchers such as [30][31][32][33][34][35][36][37][38][39][40] etc. The stated prior distributions and loss functions are defined as follows:…”
Section: Bayesian Estimationmentioning
confidence: 99%
“…is that which describes the losses incurred by making an estimate  of the true value of the parameter is α. A number of symmetric and asymmetric loss functions have been shown to be functional in so many studies including; [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36] and [37] and so forth.…”
Section: Priors and Loss Functionsmentioning
confidence: 99%