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 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 Estimation for the Parameters and Reliability Function of Basic Gompertz Distribution under Squared Log Error Loss Function

2020 ◽  
pp. 1433-1439
Author(s):  
Manahel Kh. Awad ◽  
Huda A. Rasheed
Keyword(s):  
Monte Carlo Simulation ◽  
Loss Function ◽  
Shape Parameter ◽  
Reliability Function ◽  
Shape Parameters ◽  
Likelihood Estimator ◽  
Gompertz Distribution ◽  
Mean Squared Errors ◽  
Error Loss ◽  
Bayesian Estimators

In this paper, some estimators for the unknown shape parameters and reliability function of Basic Gompertz distribution were obtained, such as Maximum likelihood estimator and some Bayesian estimators under Squared log error loss function by using Gamma and Jefferys priors. Monte-Carlo simulation was conducted to compare the performance of all estimates of the shape parameter and Reliability function, based on mean squared errors (MSE) and integrated mean squared errors (IMSE's), respectively. Finally, the discussion is provided to illustrate the results that are summarized in tables.

scholarly journals Bayesian estimation of the shape parameter of generalized Rayleigh distribution under non-informative prior

2015 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Yakubu Aliyu ◽  
Abubakar Yahaya
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Shape Parameter ◽  
Rayleigh Distribution ◽  
Skewed Distribution ◽  
Time Data ◽  
Squared Error Loss Function ◽  
Squared Error ◽  
Informative Prior ◽  
Generalized Rayleigh Distribution

<p>A decade ago, two-parameter Burr Type X distribution was introduced by Surles and Padgett [14] which was described as Generalized Rayleigh Distribution (GRD). This skewed distribution can be used quiet effectively in modelling life time data. In this work, Bayesian estimation of the shape parameter of GRD was considered under the assumption of non-informative prior. The estimates were obtained under the squared error, Entropy and Precautionary loss functions. Extensive Monte Carlo simulations were carried out to compare the performances of the Bayes estimates with that of MLEs. It was observed that the estimate under the Entropy loss function is more stable than the estimates under squared error loss function, Precautionary loss function and MLEs.</p>

scholarly journals Estimation of the scale parameter of gamma model in presence of outlier observations

1990 ◽  
Vol 13 (1) ◽  
pp. 121-127 ◽  
Author(s):  
M. E. Ghitany
Keyword(s):  
Shape Parameter ◽  
Scale Parameter ◽  
Point Estimation ◽  
Random Shape ◽  
Gamma Model ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
Testing Model ◽  
Squared Error ◽  
Two Parameter

This paper considers the Bayesian point estimation of the scale parameter for a two-parameter gamma life-testing model in presence of several outlier observations in the data. The Bayesian analysis is carried out under the assumption of squared error loss function and fixed or random shape parameter.

scholarly journals Bayesian Estimation and Prediction of Discrete Gompertz Distribution

2021 ◽  
pp. 1-21
Author(s):  
M. A. Hegazy ◽  
R. E. Abd El-Kader ◽  
A. A. El-Helbawy ◽  
G. R. Al-Dayian
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Hazard Rate ◽  
Squared Error Loss Function ◽  
Squared Error Loss ◽  
Gompertz Distribution ◽  
Estimation And Prediction ◽  
Error Loss ◽  
Squared Error ◽  
Rate Functions

In this paper, Bayesian inference is used to estimate the parameters, survival, hazard and alternative hazard rate functions of discrete Gompertz distribution. The Bayes estimators are derived under squared error loss function as a symmetric loss function and linear exponential loss function as an asymmetric loss function. Credible intervals for the parameters, survival, hazard and alternative hazard rate functions are obtained. Bayesian prediction (point and interval) for future observations of discrete Gompertz distribution based on two-sample prediction are investigated. A numerical illustration is carried out to investigate the precision of the theoretical results of the Bayesian estimation and prediction on the basis of simulated and real data. Regarding the results of simulation seems to perform better when the sample size increases and the level of censoring decreases. Also, in most cases the results under the linear exponential loss function is better than the corresponding results under squared error loss function. Two real lifetime data sets are used to insure the simulated results.

scholarly journals Bayesian Estimation for Two Parameters of Gamma Distribution under Generalized Weighted Loss Function

2019 ◽  
Vol 60 (5) ◽  
pp. 1161-1171
Author(s):  
Loaiy F. Naji ◽  
Huda A. Rasheed
Keyword(s):  
Bayesian Estimation ◽  
Gamma Distribution ◽  
Loss Function ◽  
Maximum Likelihood Estimators ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Mean Squared Errors ◽  
Scale Parameters ◽  
The Mean ◽  
Two Parameters

This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).

scholarly journals Bayesian Estimation Based on Rayleigh Progressive Type II Censored Data with Binomial Removals

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Reza Azimi ◽  
Farhad Yaghmaei
Keyword(s):  
Censored Data ◽  
Failure Time ◽  
Simulation Method ◽  
Reliability Function ◽  
Rayleigh Distribution ◽  
Type Ii ◽  
Monte Carlo Simulation Method ◽  
Bayesian Procedures ◽  
Progressive Type ◽  
Type Ii Censored Data

This study considers the estimation problem for the parameter and reliability function of Rayleigh distribution under progressive type II censoring with random removals, where the number of units removed at each failure time has a binomial distribution. We use the maximum likelihood and Bayesian procedures to obtain the estimators of parameter and reliability function of Rayleigh distribution. We also construct the confidence intervals for the parameter of Rayleigh distribution. Monte Carlo simulation method is used to generate a progressive type II censored data with binomial removals from Rayleigh distribution, and then these data are used to compute the point and interval estimations of the parameter and compare both the methods used with different random schemes.

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 Minimum Variance Unbiased Estimation in the Gompertz Distribution under Progressive Type II Censored Data with Binomial Removals

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Ashok Shanubhogue ◽  
N. R. Jain
Keyword(s):  
Censored Data ◽  
Hazard Function ◽  
Shape Parameter ◽  
Reliability Function ◽  
Minimum Variance ◽  
Unbiased Estimation ◽  
Type Ii ◽  
Gompertz Distribution ◽  
Progressive Type ◽  
Type Ii Censored Data

This paper deals with the problem of uniformly minimum variance unbiased estimation for the parameter of the Gompertz distribution based on progressively Type II censored data with binomial removals. We have obtained the uniformly minimum variance unbiased estimator (UMVUE) for powers of the shape parameter and its functions. The UMVUE of the variance of these estimators is also given. The UMVUE of (i) pdf, (ii) cdf, (iii) reliability function, and (iv) hazard function of the Gompertz distribution is derived. Further, an exact % confidence interval for the th quantile is obtained. The UMVUE of pdf is utilized to obtain the UMVUE of . An illustrative numerical example is presented.

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).  

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