Comparison of Bayes Estimators for Parameter and Relia-bility Function for Inverse Rayleigh Distribution by Using Generalized Square Error Loss Function

Rasheed, Huda A.

doi:10.23851/mjs.v28i2.512

Cited by 5 publications

(7 citation statements)

References 3 publications

(4 reference statements)

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“…Meanwhile, the estimator for the scale parameter ( ) using the Bayes Generalized SELF method will be described as follows [9]:…”

Section: Bayesian Generalized Self Methodsmentioning

confidence: 99%

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Bayesian Generalized Self Method to Estimate Scale Parameter of Invers Rayleigh Distribution

Yanuar

Febriyuni²,

Hg³

2021

CAUCHY

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The purposes of this study are to estimate the scale parameter of Invers Rayleigh distribution under MLE and Bayesian Generalized square error loss function (SELF). The posterior distribution is considered to use two types of prior, namely Jeffrey’s prior and exponential distribution. The proposed methods are then employed in the real data. Several criteria for the selection model are considered in order to identify the method which results in a suitable value of parameter estimated. This study found that Bayesian Generalized SELF under Jeffrey’s prior yielded better estimation values that MLE and Bayesian Generalized SELF under exponential distribution.

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“…Meanwhile, the estimator for the scale parameter ( ) using the Bayes Generalized SELF method will be described as follows [9]:…”

Section: Bayesian Generalized Self Methodsmentioning

confidence: 99%

“…Dey [8] obtained Bayesian estimates of an inverse Rayleigh distribution using squared error and LINEX loss functions. Meanwhile, Rasheed [9] designed some Bayesian estimators for the parameter scale and reliability function of the inverse Rayleigh distribution under the Generalized squared error loss function (SELF).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Generalized Self Method to Estimate Scale Parameter of Invers Rayleigh Distribution

Yanuar

Febriyuni²,

Hg³

2021

CAUCHY

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“…Rasheed H. A. (2011) [7] estimated the scale parameter of the Rayleigh distribution by applying the Bayes estimators under different loss functions (using Jeffrey prior information). Dey S. (2012) [2] Bayes estimators are obtained under symmetric and asymmetric linear exponential loss functions using a noninformative prior.…”

Section: Introductionmentioning

confidence: 99%

“…Dey S. (2012) [2] Bayes estimators are obtained under symmetric and asymmetric linear exponential loss functions using a noninformative prior. Oayd R. G. (2012) [6] derived the standard Bayes estimators for scale parameter and reliability function and failure rate function of Rayleigh distribution. Kazem T. H., Rashid H. A., Al Obeidi N.…”

Section: Introductionmentioning

confidence: 99%

A COMPARISON OF BAYESIAN APPROACH WITH Maximum Likelihood Method To Estimate Parameter For Rayleigh Distribution

Najm A. Oleiwi Al-Ghezi

2023

JWSM

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The main objective of this study is to obtain and compare the performance of "Maximum likelihood estimator (MLE) and Bayesian estimators of the scale parameter of the Rayleigh distribution. In order to get better understanding in our Bayesian analysis we consider informative prior as well as non-informative prior using Jeffery prior information under loss functions (modified squred error loss function, Quadratic error loss function) to find the best method for estimation, which used the samples size ( 10, 20, 30, 50, 100). The comparison of the estimators, based on their mean squared errors (MSE's), we obtain that, MinMSE is the best estimator, while the performance of Bayes estimator under modified squared error loss function with non-informative prior with a value of the scale parameter ( ) is the best estimator comparing to other for all simple size.

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“…Lu and Tau, Caeiro et al, 20 and Kim et al 21 studied several least squares estimators and Brazauskas and Serfling 22 and Vandewalle et al 23 introduced robust estimators of the shape parameter. Bayesian estimators can be found in Arnold and Press, 24 Rasheed and Al‐Gazi, 25 and Han 26 . Caeiro and Gomes 27 and Munir et al 28 considered probability weighted moment estimators.…”

Section: Introductionmentioning

confidence: 99%

A new class of estimators for the shape parameter of a Pareto model

Mateus

Caeiro

2020

Comp and Math Methods

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The Pareto model, first used in socioeconomic problems, has successfully been applied in many other areas such as astronomy, biology, bibliometrics, demography, insurance, or risk management. Although there are several variants of this distribution, the current study focuses on the classic Pareto distribution, also known as the Pareto type I distribution. We propose a new class of estimators for the Pareto shape parameter, obtained through a modification of the probability weighted moment method, called the log generalized probability weighted moments method. In addition to the asymptotic distribution, Monte Carlo simulations were performed to analyze the finite sample behavior of the proposed new estimators. A comparison with the most used estimators, such as the moment, the maximum likelihood, the least squares, and the probability weighted moments estimators was also performed. In addition, the estimators were used to construct asymptotic confidence intervals. To illustrate an application of the different estimation methods to a real data set from a clinical trial complete the article. Results indicate an overall good performance of the new proposed class.

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Comparison of Bayes Estimators for Parameter and Relia-bility Function for Inverse Rayleigh Distribution by Using Generalized Square Error Loss Function

Cited by 5 publications

References 3 publications

Bayesian Generalized Self Method to Estimate Scale Parameter of Invers Rayleigh Distribution

Bayesian Generalized Self Method to Estimate Scale Parameter of Invers Rayleigh Distribution

A COMPARISON OF BAYESIAN APPROACH WITH Maximum Likelihood Method To Estimate Parameter For Rayleigh Distribution

A new class of estimators for the shape parameter of a Pareto model

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