scholarly journals مقارنة مقدرات بيز لدالة المعولية لتوزيع باريتو من النوع الاول باستعمال دوال معلوماتية مضاعفة مختلفة

2019 ◽  
Vol 25 (113) ◽  
Author(s):  
جنان عباس ناصر
Keyword(s):  
Prior Distribution ◽  
Reliability Function ◽  
Bayes Estimation ◽  
Erlang Distribution ◽  
Type I ◽  
Informative Priors ◽  
Squared Error Loss Function ◽  
True Value ◽  
Squared Error ◽  
Simulation Results

The comparison of double informative priors which are assumed for the reliability function of Pareto type I distribution. To estimate the reliability function of Pareto type I distribution by using Bayes estimation, will be  used two different kind of information in the Bayes estimation; two different priors have been selected for the parameter of Pareto  type I distribution . Assuming distribution of three double prior’s chi- gamma squared distribution, gamma - erlang distribution, and erlang- exponential distribution as double priors. The results of the derivaties of these estimators under the squared error loss function with two different double priors. Using the simulation technique, to compare the performance for each estimator, several cases from pareto type I distribution for data generating, and for different samples sizes (small, medium, and large). It has been obtained from the simulation results the double prior distribution  of gamma-erlang distribution with give a good estimation for reliability function when the true value for for all .Also the double prior distribution chi- gamma square distribution with give good estimation for reliability function when the true value for all t. And the same thing for with the values of the parameters and for all t except t=1.3. It has obtained a good estimation for reliability function (), when the double prior distribution is chi-gamma square distribution with at the true value for for all t.

scholarly journals A Comparison of Bayes Estimators for the parameter of Rayleigh Distribution with Simulation

2018 ◽  
Vol 24 (106) ◽  
pp. 49
Author(s):  
جنان عباس ناصر
Keyword(s):  
Loss Function ◽  
Prior Distribution ◽  
Mean Squared Error ◽  
Rayleigh Distribution ◽  
Square Root ◽  
Bayes Estimators ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
True Value ◽  
Squared Error

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared error loss function and weighted squared error loss function) in the cases of the three different sets of prior distributions .Simulations is employed to obtain results. And determine the best estimator according to the smallest value of mean squared error and weighted mean squared error. We found  that the best estimation for the parameter for all sample sizes (n) , when the double prior distribution for  is SRIG - the natural conjugate family of priors distribution with values (a=5, b=0.5, =8, =0.5) and (a=8, b=1, =5, =1) for the  true value of  respectively .Also ,we obtained the best estimation for  when the double prior distribution for  is the natural conjugate family of priors-non-informative distribution with values(=0.5, =5, c=1) for  the true value of ().  

scholarly journals The Comparison Between the MLE and Standard Bayes Estimators of the Reliability Function of Exponential Distribution

2019 ◽  
Vol 32 (1) ◽  
pp. 103
Author(s):  
Mohammed Jamel Ali ◽  
Hazim Mansoor Gorgees
Keyword(s):  
Loss Function ◽  
Reliability Function ◽  
Loss Functions ◽  
Mean Square ◽  
Bayes Estimators ◽  
Squared Error Loss Function ◽  
Monte Carlo Simulation Technique ◽  
Error Loss ◽  
Squared Error ◽  
The One

     In this paper, a Monte Carlo Simulation technique is used to compare the performance of MLE and the standard Bayes estimators of the reliability function of the one parameter exponential distribution.Two types of loss functions are adopted, namely, squared error  loss function (SELF) and modified square error loss function (MSELF) with informative and non- informative prior. The criterion integrated mean square error (IMSE) is employed to assess the performance of such estimators .

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

2018 ◽  
Vol 28 (2) ◽  
pp. 162
Author(s):  
Huda A. Rasheed
Keyword(s):  
Loss Function ◽  
Scale Parameter ◽  
Reliability Function ◽  
Rayleigh Distribution ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
Mean Squared Errors ◽  
Error Loss ◽  
Squared Error ◽  
Informative Prior

In the current study, we have been derived some Basyian estimators for the parameter and relia-bility function of the inverse Rayleigh distribution under Generalized squared error loss function. In order to get the best understanding of the behavior of Bayesian analysis, we consider non-informative prior for the scale parameter using Jefferys prior Information as well as informative prior density represented by Gamma distribution. Monte-Carlo simulation have been employed to compare the behavior of different estimates for the scale parameter and reliability function of in-verse Rayleigh distribution based on mean squared errors and Integrated mean squared errors, respectively. In the current study, we observed that more occurrence of Bayesian estimate using Generalized squared error loss function using Gamma prior is better than other estimates for all cases

scholarly journals Bayesian and Non - Bayesian Inference for Shape Parameter and Reliability Function of Basic Gompertz Distribution

2020 ◽  
Vol 17 (3) ◽  
pp. 0854
Author(s):  
Manahel Awad ◽  
Huda Rashed
Keyword(s):  
Shape Parameter ◽  
Simulation Method ◽  
Reliability Function ◽  
Monte Carlo Simulation Method ◽  
Scale Invariant ◽  
Squared Error Loss Function ◽  
Gompertz Distribution ◽  
Mean Squared Errors ◽  
Squared Error ◽  
Informative Prior

In this paper, some estimators of the unknown shape parameter and reliability function  of Basic Gompertz distribution (BGD) have been obtained, such as MLE, UMVUE, and MINMSE, in addition to estimating Bayesian estimators under Scale invariant squared error loss function assuming informative prior represented by Gamma distribution and non-informative prior by using Jefferys prior. Using Monte Carlo simulation method, these estimators of the shape parameter and R(t), have been compared based on mean squared errors and integrated mean squared, respectively

scholarly journals Comparing Different Estimators for the shape Parameter and the Reliability function of Kumaraswamy Distribution

2019 ◽  
Vol 25 (116) ◽  
pp. 199-225
Author(s):  
Jinan Abbas Naser Al-Obedy
Keyword(s):  
Shape Parameter ◽  
Reliability Function ◽  
Bayes Estimation ◽  
Likelihood Method ◽  
Prior Distributions ◽  
Squared Error Loss Function ◽  
Kumaraswamy Distribution ◽  
Chi Squared ◽  
Two Parameters ◽  
Mean Square Errors

In this paper, we used maximum likelihood method and the Bayesian method to estimate the shape parameter (θ), and reliability function (R(t)) of the Kumaraswamy distribution with two parameters l , θ (under assuming the exponential distribution, Chi-squared distribution and Erlang-2 type distribution as prior distributions), in addition to that we used method of moments for estimating the parameters of the prior distributions. Bayes estimators derived under the squared error loss function. We conduct simulation study, to compare the performance for each estimator, several values of the shape parameter (θ) from Kumaraswamy distribution for data-generating, for different samples sizes (small, medium, and large). Simulation results have shown that the Best method is the Bayes estimation according to the smallest values of mean square errors(MSE) for all samples sizes (n).  

scholarly journals Bayes Estimators for the Parameter of the Inverted Exponential Distribution Under different Double informative priors

2018 ◽  
Vol 24 (103) ◽  
pp. 18
Author(s):  
جنان عباس ناصر
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Bayes Estimation ◽  
Erlang Distribution ◽  
Mean Square ◽  
Informative Priors ◽  
Chi Squared ◽  
Mean Square Errors

In this paper, we present a comparison of double informative priors which are assumed for the parameter of inverted exponential distribution.To estimate the parameter of inverted exponential distribution by using Bayes estimation ,will be  used two different kind of information in the Bayes estimation; two different priors have been selected for the parameter of inverted exponential distribution. Also assumed Chi-squared - Gamma distribution, Chi-squared - Erlang distribution, and- Gamma- Erlang distribution as double priors. The results are the derivations of these estimators under the squared error loss function with three different double priors. Additionally Maximum likelihood estimation method (MLE) was used  to estimate the parameter of inverted exponential distribution .We used simulation technique, to compare the performance for each estimator, several cases from inverted exponential distribution for data generating, for different samples sizes (small, medium, and large).Simulation results shown that the best method is the bayes  estimation according to the smallest values of mean square errors( MSE) for all samples sizes (n) comparative to the estimated values by using MLE . According to obtained results, we see that when the double prior distribution for  is Gamma- Erlang distribution for some values for the parameters a, b & given the best results according to the smallest values of mean square errors (MSE) comparative to the same values which obtained by using Maximum likelihood estimation (MLE) for the assuming true values for and for all samples sizes.  

scholarly journals The Comparison Between Standard Bayes Estimators of the Reliability Function of Exponential Distribution

2018 ◽  
Vol 31 (3) ◽  
pp. 135 ◽  
Author(s):  
Mohammed Jamel Ali ◽  
Hazim Mansoor Gorgees ◽  
Adel Abdul Kadhim Hussein
Keyword(s):  
Exponential Distribution ◽  
Loss Function ◽  
Reliability Function ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Squared Error Loss Function ◽  
Monte Carlo Simulation Technique ◽  
Error Loss ◽  
Squared Error ◽  
The One

   In this paper, a Monte Carlo Simulation technique is used to compare the performance of the standard Bayes estimators of the reliability function of the one parameter exponential distribution .Three types of loss functions are adopted, namely, squared error  loss function (SELF) ,Precautionary error loss function (PELF) andlinear exponential error  loss function(LINEX) with informative and non- informative prior .The criterion integrated mean square error (IMSE) is employed to assess the performance of such estimators

scholarly journals E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 636 ◽  
Author(s):  
Ali Algarni ◽  
Abdullah M. Almarashi ◽  
Hassan Okasha ◽  
Hon Keung Tony Ng
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Scale Parameter ◽  
Type I ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
Type I Censoring ◽  
Squared Error ◽  
Censoring Scheme ◽  
Bayesian Estimators

In this paper, E-Bayesian estimation of the scale parameter, reliability and hazard rate functions of Chen distribution are considered when a sample is obtained from a type-I censoring scheme. The E-Bayesian estimators are obtained based on the balanced squared error loss function and using the gamma distribution as a conjugate prior for the unknown scale parameter. Also, the E-Bayesian estimators are derived using three different distributions for the hyper-parameters. Some properties of E-Bayesian estimators based on balanced squared error loss function are discussed. A simulation study is performed to compare the efficiencies of different estimators in terms of minimum mean squared errors. Finally, a real data set is analyzed to illustrate the applicability of the proposed estimators.

E-Bayes Estimation of Laplace Distribution Parameter under Q-Symmetric Entropy Loss Function

2014 ◽  
Vol 596 ◽  
pp. 301-304
Author(s):  
Huan Bin Liu ◽  
Tong Yin ◽  
Cheng Wang
Keyword(s):  
Numerical Simulation ◽  
Loss Function ◽  
Beta Distribution ◽  
Prior Distribution ◽  
Laplace Distribution ◽  
Hierarchical Bayes ◽  
Distribution Parameter ◽  
Bayes Estimation ◽  
Entropy Loss ◽  
Simulation Results

This paper provides the E-Bayes estimation and hierarchical Bayes estimation of Laplace distribution parameter under q-symmetric entropy loss function when the prior distribution is Beta distribution. The relation between E-Bayes estimation and hierarchical Bayes estimation is discussed. Numerical simulation results show that these two types of estimation are progressively equal, and E-Bayes estimation is more convenient in terms of calculation.

scholarly journals The effect of loss functions on empirical Bayes reliability analysis

1999 ◽  
Vol 4 (6) ◽  
pp. 539-560 ◽  
Author(s):  
Vincent A. R. Camara ◽  
Chris P. Tsokos
Keyword(s):  
Loss Function ◽  
Empirical Bayes ◽  
Reliability Function ◽  
Loss Functions ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
Error Loss ◽  
Squared Error ◽  
Reliability Estimates ◽  
Logarithmic Loss

The aim of the present study is to investigate the sensitivity of empirical Bayes estimates of the reliability function with respect to changing of the loss function. In addition to applying some of the basic analytical results on empirical Bayes reliability obtained with the use of the “popular” squared error loss function, we shall derive some expressions corresponding to empirical Bayes reliability estimates obtained with the Higgins–Tsokos, the Harris and our proposed logarithmic loss functions. The concept of efficiency, along with the notion of integrated mean square error, will be used as a criterion to numerically compare our results.It is shown that empirical Bayes reliability functions are in general sensitive to the choice of the loss function, and that the squared error loss does not always yield the best empirical Bayes reliability estimate.

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