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 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 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

scholarly journals E-Bayesian and Bayesian Estimation for the Lomax Distribution under Weighted Composite LINEX Loss Function

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Afrah Al-Bossly
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Shape Parameter ◽  
Likelihood Estimation ◽  
Loss Functions ◽  
Bayesian Estimator ◽  
Linex Loss Function ◽  
Lomax Distribution ◽  
Linex Loss ◽  
Asymmetric Loss Function

The main contribution of this work is the development of a compound LINEX loss function (CLLF) to estimate the shape parameter of the Lomax distribution (LD). The weights are merged into the CLLF to generate a new loss function called the weighted compound LINEX loss function (WCLLF). Then, the WCLLF is used to estimate the LD shape parameter through Bayesian and expected Bayesian (E-Bayesian) estimation. Subsequently, we discuss six different types of loss functions, including square error loss function (SELF), LINEX loss function (LLF), asymmetric loss function (ASLF), entropy loss function (ENLF), CLLF, and WCLLF. In addition, in order to check the performance of the proposed loss function, the Bayesian estimator of WCLLF and the E-Bayesian estimator of WCLLF are used, by performing Monte Carlo simulations. The Bayesian and expected Bayesian by using the proposed loss function is compared with other methods, including maximum likelihood estimation (MLE) and Bayesian and E-Bayesian estimators under different loss functions. The simulation results show that the Bayes estimator according to WCLLF and the E-Bayesian estimator according to WCLLF proposed in this work have the best performance in estimating the shape parameters based on the least mean averaged squared error.

scholarly journals Estimating the Common Mean of k Normal Populations with Known Variance

2017 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
N. Sanjari Farsipour ◽  
A. Asgharzadeh
Keyword(s):  
Loss Function ◽  
Linex Loss Function ◽  
Common Mean ◽  
Normal Populations ◽  
Squared Error ◽  
Linex Loss ◽  
The Common

Consider the problem of estimating the common mean of knormal populations with known variances. We study the admisibility of the Best linear Risk Unbiased Equivariant (BLRUE)estimator of the common mean of k normalpopulations underthe squared error and LINEX loss function when the variancesare known.

scholarly journals Estimating the Common Mean of k Normal Populations with Known Variance

2016 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
N. Sanjari Farsipour ◽  
A. Asgharzadeh
Keyword(s):  
Loss Function ◽  
Linex Loss Function ◽  
Common Mean ◽  
Normal Populations ◽  
Squared Error ◽  
Linex Loss ◽  
The Common

Consider the problem of estimating the common mean of knormal populations with known variances. We study the admisibility of the Best linear Risk Unbiased Equivariant (BLRUE)estimator of the common mean of k normalpopulations underthe squared error and LINEX loss function when the variancesare known.

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