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Performance of a Class of Bayes Shrinkage Estimators Based on Rayleigh Record Data under Reflected Gamma Loss Function
Author(s) -
Mehran Naghizadeh Qomi,
Sanku Dey,
Monir Fathollahi
Publication year - 2019
Publication title -
journal of the iranian statistical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.293
H-Index - 6
eISSN - 2538-189X
pISSN - 1726-4057
DOI - 10.29252/jirss.18.2.155
Subject(s) - shrinkage , estimator , bayes' theorem , mathematics , rayleigh scattering , shrinkage estimator , class (philosophy) , statistics , function (biology) , algorithm , artificial intelligence , computer science , bayesian probability , minimax estimator , physics , optics , minimum variance unbiased estimator , evolutionary biology , biology
This article addresses the problem of Bayesian shrinkage estimation for the Rayleigh scale parameter based on record values under the reflected gamma loss (RGL) function. A class of Bayesian shrinkage estimators using prior point information is constructed. The risk functions of the maximum likelihood estimator (MLE) and proposed Bayesian shrinkage estimator are derived under the RGL function. The performance of Bayesian shrinkage estimator is compared with the MLE numerically and graphically. One data set has been analyzed to illustrate the performance of the Bayesian shrinkage estimator.

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