首页> 外文期刊>International Journal of Advanced Statistics and Probability >Quasi-E-Bayesian criteria versus quasi-Bayesian, quasi-hierarchical Bayesian and quasi-empirical Bayesian methods for estimating the scale parameter of the Erlang distribution
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Quasi-E-Bayesian criteria versus quasi-Bayesian, quasi-hierarchical Bayesian and quasi-empirical Bayesian methods for estimating the scale parameter of the Erlang distribution

机译:拟E贝叶斯准则与拟贝叶斯,拟层级贝叶斯和拟经验贝叶斯方法估计Erlang分布的比例参数

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This paper proposes a new modification for the E-Bayesian method of estimation to introduce a new technique namely Quasi E-Bayesian method (or briefly QE-Bayesian). The suggested criteria built in replacing the likelihood function by the quasi likelihood function in the E-Bayesian technique. This study is devoted to evaluate the performance of the new method versus the quasi-Bayesian, quasi-hierarchical Bayesian and quasi-empirical Bayesian approaches in estimating the scale parameter of the Erlang distribution. All estimators are obtained under symmetric loss function [squared error loss (SELF))] and four different asymmetric loss functions [Precautionary loss function (PLF), entropy loss function (ELF), Degroot loss function (DLF) and quadratic loss function (QLF)]. The properties of the QE-Bayesian estimates are introduced and the relations between the QE-Bayes and quasi-hierarchical Bayes estimates are discussed. Comparisons among all estimators are performed in terms of mean square error (MSE) via Monte Carlo simulation.
机译:本文提出了对E-贝叶斯估计方法的新修改,以引入一种新技术,即准E-贝叶斯方法(或简称QE-贝叶斯方法)。在E-贝叶斯技术中用拟似然函数代替似然函数而建立的建议标准。这项研究致力于评估新方法与准贝叶斯方法,准分层贝叶斯方法和准经验贝叶斯方法在估计Erlang分布的尺度参数方面的性能。所有估计量都是在对称损失函数[平方误差损失(SELF))和四个不同的非对称损失函数[预防损失函数(PLF),熵损失函数(ELF),地根损失函数(DLF)和二次损失函数(QLF)下获得的)]。介绍了QE-贝叶斯估计的性质,并讨论了QE-贝叶斯和准分层贝叶斯估计之间的关系。所有估算器之间的比较均通过蒙特卡洛模拟以均方误差(MSE)进行。

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