scholarly journals Comparison of estimates using censored samples from Gompertz model: Bayesian, E-Bayesian, hierarchical Bayesian and empirical Bayesian schemes

2016 ◽  
Vol 4 (1) ◽  
pp. 47 ◽  
Author(s):  
Hesham Reyad ◽  
Adil Mousa Younis ◽  
Amal Alsir Alkhedir
Keyword(s):  
Loss Function ◽  
Gompertz Model ◽  
Quadratic Loss ◽  
Hierarchical Bayesian ◽  
Empirical Bayesian ◽  
Bayesian Hierarchical ◽  
Censored Samples ◽  
Linex Loss ◽  
Symmetric Loss ◽  
Bayesian Criteria

<p>This paper aims to introduce a comparative study for the E-Bayesian criteria with three various Bayesian approaches; Bayesian, hierarchical Bayesian and empirical Bayesian. This study is concerned to estimate the shape parameter and the hazard function of the Gompertz distribution based on type-II censoring. All estimators are obtained under symmetric loss function [squared error loss (SELF))] and three different asymmetric loss functions [quadratic loss function (QLF), entropy loss function (ELF) and LINEX loss function (LLF)]. Comparisons among all estimators are achieved in terms of mean square error (MSE) via Monte Carlo simulation.</p>

scholarly journals Quasi-E-Bayesian criteria versus quasi-Bayesian, quasi-hierarchical Bayesian and quasi-empirical Bayesian methods for estimating the scale parameter of the Erlang distribution

2016 ◽  
Vol 4 (1) ◽  
pp. 62 ◽  
Author(s):  
Hesham Reyad ◽  
Adil Younis ◽  
Amal Alkhedir
Keyword(s):  
Loss Function ◽  
Bayesian Method ◽  
Scale Parameter ◽  
Likelihood Function ◽  
Hierarchical Bayes ◽  
Erlang Distribution ◽  
Quadratic Loss ◽  
Hierarchical Bayesian ◽  
Empirical Bayesian ◽  
Symmetric Loss

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.

scholarly journals E-Bayesian analysis of the Gumbel type-ii distribution under type-ii censored scheme

2015 ◽  
Vol 3 (2) ◽  
pp. 108 ◽  
Author(s):  
Hesham Reyad ◽  
Soha Othman Ahmed
Keyword(s):  
Loss Function ◽  
Shape Parameter ◽  
Quadratic Loss ◽  
Type Ii ◽  
Linex Loss Function ◽  
Censored Samples ◽  
Squared Error ◽  
Linex Loss ◽  
Symmetric Loss ◽  
Bayesian Estimators

<p>This paper seeks to focus on Bayesian and E-Bayesian estimation for the unknown shape parameter of the Gumbel type-II distribution based on type-II censored samples. These estimators are obtained under symmetric loss function [squared error loss (SELF))] and various asymmetric loss functions [LINEX loss function (LLF), Degroot loss function (DLF), Quadratic loss function (QLF) and minimum expected loss function (MELF)]. Comparisons between the E-Bayesian estimators with the associated Bayesian estimators are investigated through a simulation study.</p>

scholarly journals Bayesian and E-Bayesian estimation for the Kumaraswamy distribution based on type-ii censoring

2016 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Hesham Reyad ◽  
Soha Othman Ahmed
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Quadratic Loss ◽  
Type Ii ◽  
Linex Loss Function ◽  
Kumaraswamy Distribution ◽  
Squared Error ◽  
Linex Loss ◽  
Symmetric Loss ◽  
Bayesian Estimators

<p>This paper introduces the Bayesian and E-Bayesian estimation for the shape parameter of the Kumaraswamy distribution based on type-II censored schemes. These estimators are derived under symmetric loss function [squared error loss (SELF))] and three asymmetric loss functions [LINEX loss function (LLF), Degroot loss function (DLF) and Quadratic loss function (QLF)]. Monte Carlo simulation is performed to compare the E-Bayesian estimators with the associated Bayesian estimators in terms of Mean Square Error (MSE).</p>

scholarly journals Bayesian Inference of Burr Type VIII Distribution Based on Censored Samples

2014 ◽  
Vol 2014 ◽  
pp. 1-21
Author(s):  
Navid Feroz
Keyword(s):  
Bayesian Inference ◽  
Loss Function ◽  
Simulation Study ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Quadratic Loss ◽  
Bayes Estimators ◽  
Type Viii ◽  
Censored Samples ◽  
Made In

This paper is concerned with estimation of the parameter of Burr type VIII distribution under a Bayesian framework using censored samples. The Bayes estimators and associated risks have been derived under the assumption of five priors and three loss functions. The comparison among the performance of different estimators has been made in terms of posterior risks. A simulation study has been conducted in order to assess and compare the performance of different estimators. The study proposes the use of inverse Levy prior based on quadratic loss function for Bayes estimation of the said parameter.

scholarly journals On estimation of gumbel type-ii with numerous kinds of linex loss function

2021 ◽  
Vol 1897 (1) ◽  
pp. 012008
Author(s):  
Nadia J. Fezaa Al-Obedy ◽  
Wafaa S. Hasanain
Keyword(s):  
Loss Function ◽  
Type Ii ◽  
Linex Loss Function ◽  
Linex Loss

Data Processing and Technology Application in Bayes and Empirical Bayes Reliability Analysis of Parameter of Ailamujia Distribution

2014 ◽  
Vol 951 ◽  
pp. 249-252
Author(s):  
Hui Zhou
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimators ◽  
Conjugate Prior ◽  
Likelihood Estimator ◽  
Empirical Bayesian ◽  
Technology Application ◽  
Inverse Gamma ◽  
Squared Error ◽  
Linex Loss ◽  
Empirical Bayes Estimators

The estimation of the parameter of the ЭРланга distribution is discussed based on complete samples. Bayes and empirical Bayesian estimators of the parameter of the ЭРланга distribution are obtained under squared error loss and LINEX loss by using conjugate prior inverse Gamma distribution. Finally, a Monte Carlo simulation example is used to compare the Bayes and empirical Bayes estimators with the maximum likelihood estimator.

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