Classical and Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution

Ahmad, Kaisar; Ahmad, Sajjad; Ahmed, Aquil

doi:10.1155/2016/7581918

Cited by 19 publications

(22 citation statements)

References 4 publications

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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%

A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications

Ahmad¹,

Ahmad²

2020

Statistical Methodologies

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In this chapter, Erlang distribution is considered. For parameter estimation, maximum likelihood method of estimation, method of moments and Bayesian method of estimation are applied. In Bayesian methodology, different prior distributions are employed under various loss functions to estimate the rate parameter of Erlang distribution. At the end the simulation study is conducted in R-Software to compare these methods by using mean square error with varying sample sizes. Also the real life applications are examined in order to compare the behavior of the data sets in the parametric estimation. The comparison is also done among the different loss functions.

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Section: Bayesian Methods Of Estimationmentioning

confidence: 99%

A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications

Ahmad¹,

Ahmad²

2020

Statistical Methodologies

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“…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%

Bayesian Analysis of Weibull-Lindley Distribution Using Different Loss Functions

Eraikhuemen

Bamigbala

Magaji

et al. 2020

AJARR

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In the present paper, a three-parameter Weibull-Lindley distribution is considered for Bayesian analysis. The estimation of a shape parameter of Weibull-Lindley distribution is obtained with the help of both the classical and Bayesian methods. Bayesian estimators are obtained by using Jeffrey's prior, uniform prior and Gamma prior under square error loss function, quadratic loss function and Precautionary loss function. Estimation by the method of Maximum likelihood is also discussed. These methods are compared by using mean square error through simulation study with varying parameter values and sample sizes. AJARR, 8(4): 28-41, 2020; Article no.AJARR.54833 29 Original Research Article

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“…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%

Bayesian Analysis of a Shape Parameter of the Weibull-Frechet Distribution

Ieren

Chukwu

2018

AJPAS

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In this paper, we estimate a shape parameter of the Weibull-Frechet distribution by considering the Bayesian approach under two non-informative priors using three different loss functions. We derive the corresponding posterior distributions for the shape parameter of the Weibull-Frechet distribution assuming that the other three parameters are known. The Bayes estimators and associated posterior risks have also been derived using the three different loss functions. The performance of the Bayes estimators are evaluated and compared using a comprehensive simulation study and a real life application to find out the combination of a loss function and a prior having the minimum Bayes risk and hence producing the best results. In conclusion, this study reveals that in order to estimate the parameter in question, we should use quadratic loss function under either of the two non-informative priors used in this study.

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Classical and Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution

Cited by 19 publications

References 4 publications

A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications

A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications

Bayesian Analysis of Weibull-Lindley Distribution Using Different Loss Functions

Bayesian Analysis of a Shape Parameter of the Weibull-Frechet Distribution

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