scholarly journals Bayes Estimation of a Two-Parameter Geometric Distribution under Multiply Type II Censoring

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
J. B. Shah ◽  
M. N. Patel
Keyword(s):  
Geometric Distribution ◽  
Correct Choice ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Type Ii ◽  
Expected Loss ◽  
Bayes Estimators ◽  
Linex Loss ◽  
Type Ii Censored Data ◽  
Two Parameter

We derive Bayes estimators of reliability and the parameters of a two- parameter geometric distribution under the general entropy loss, minimum expected loss and linex loss, functions for a noninformative as well as beta prior from multiply Type II censored data. We have studied the robustness of the estimators using simulation and we observed that the Bayes estimators of reliability and the parameters of a two-parameter geometric distribution under all the above loss functions appear to be robust with respect to the correct choice of the hyperparameters a(b) and a wrong choice of the prior parameters b(a) of the beta prior.

scholarly journals On Truncated Zeghdoudi Distribution : Posterior Analysis under Different Loss Functions for Type II Censored Data

2021 ◽  
pp. 497-508
Author(s):  
Hamida Talhi ◽  
Hiba Aiachi
Keyword(s):  
Censored Data ◽  
Maximum Likelihood Estimators ◽  
Real Data ◽  
Loss Functions ◽  
Model Parameters ◽  
Type Ii ◽  
Bayes Estimators ◽  
Quadratic Entropy ◽  
Type Ii Censored Data ◽  
Bayesian Estimators

We perform a Bayesian analysis of the upper trunacated Zeghdoudi distribution based on type II censored data. Using various loss functions including the generalised quadratic, entropy and Linex functions, we obtain Bayes estimators and the corresponding posterior risks. As tractable analytical forms of these estimators is out of reach, we propose the use of simulations based on Markov chain Monte-carlo methods to study their performance. Given nitial values of model parameters, we also obtain maximum likelihood estimators. Using Pitmanw closeness criterion and integrated mean square error we  compare their performance with those of the Bayesian estimators. Finally, we illustrate our approach through an example using a set of real data.

scholarly journals Estimation of the Parameters for the Exponentiated Weibull Distribution Based on Progressive Type-II Censoring Scheme

2020 ◽  
Vol 9 (5) ◽  
pp. 1
Author(s):  
Mohammed Mohammed Ahmed Almazah
Keyword(s):  
Weibull Distribution ◽  
Loss Functions ◽  
Type Ii ◽  
Bayes Estimators ◽  
Shape Parameters ◽  
Censored Samples ◽  
Linex Loss ◽  
Progressive Type ◽  
Exponentiated Weibull Distribution ◽  
Exponentiated Weibull

The main objective of the present study is to find the estimation of the two Exponentiated Weibull distribution parameters, based on progressive Type II censored samples. The maximum likelihood and Bayes estimators for the two shape parameters and the scale parameter of the exponentiated Weibull lifetime model were derived. Bayes estimators was obtained by using both the symmetric and asymmetric loss functions via squared error loss and linex loss functions This was done with respect to the conjugate priors for two shape parameters. We used an approximation based on the Lindley (Trabajos de Estadistca) method for obtaining Bayes estimates under these loss functions. The different proposed estimators have been compared through an extensive simulation studies. Bayes ratings also turned out to be better than MLE. Whatever the sample sizes are, we get the same results.

scholarly journals Bayes Estimation Under Different Censoring Patterns On Constant-Stress PALT

2018 ◽  
Vol 47 (4) ◽  
pp. 60-74
Author(s):  
Gyan Prakash
Keyword(s):  
Bayes Estimation ◽  
Constant Stress ◽  
Accelerated Life Test ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Life Test ◽  
Gompertz Distribution ◽  
Accelerated Life ◽  
Special Cases ◽  
Two Parameter

Two-Parameter Gompertz distribution is considered here for the Bayesian inference under the Constant-Stress Partially Accelerated Life Test (CS-PALT). The first-failure Progressive (FFP) censoring pattern and its special cases have used for the analysis based on Bayes estimators of all the parameters under two different asymmetric loss functions and their special cases. A simulation study has carried out for the numerical analysis.

scholarly journals ON THE BAYESIAN ANALYSIS OF EXTENDED WEIBULL-GEOMETRIC DISTRIBUTION

2019 ◽  
pp. 115-137
Author(s):  
Azeem Ali ◽  
Sajid Ali ◽  
Shama Khaliq
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Geometric Distribution ◽  
Real Life ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Informative Priors ◽  
Bayes Estimates

The paper deals with the Bayes estimation of Extended Weibull-Geometric (EWG) distribution. In particular, we discuss Bayes estimators and their posterior risks using the noninformative and informative priors under different loss functions. Since the posterior summaries cannot be obtained analytically, we adopt Markov Chain Monte Carlo (MCMC) technique to assess the performance of Bayes estimates for different sample sizes. A real life example is also part of this study.  

scholarly journals Estimating the Parameters of the Exponential-Geometric distribution based on progressively type-II censored data

2018 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
Aisha Fayomi ◽  
Hamdah Al-Shammari
Keyword(s):  
Censored Data ◽  
Geometric Distribution ◽  
Asymptotic Variance ◽  
Parameters Estimation ◽  
Type Ii ◽  
Progressive Type ◽  
The Em Algorithm ◽  
Asymptotic Confidence Intervals ◽  
Distribution Parameters ◽  
Type Ii Censored Data

This paper deals with the problem of parameters estimation of the Exponential-Geometric (EG) distribution based on progressive type-II censored data. It turns out that the maximum likelihood estimators for the distribution parameters have no closed forms, therefore the EM algorithm are alternatively used. The asymptotic variance of the MLEs of the targeted parameters under progressive type-II censoring is computed along with the asymptotic confidence intervals. Finally, a simple numerical example is given to illustrate the obtained results.

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.

Reliability Estimation for the Two-Parameter Exponential Family Based on Progressively Type-II Censored Data

2018 ◽  
Vol 25 (01) ◽  
pp. 1850005
Author(s):  
Wenhao Gui
Keyword(s):  
Reliability Function ◽  
Reliability Estimation ◽  
Type Ii ◽  
Prior Density ◽  
Monte Carlo Simulation Study ◽  
Censored Samples ◽  
Type Ii Censored Data ◽  
Two Parameters ◽  
Parameter Case ◽  
Two Parameter

In this paper, we deal with the problem of estimating the reliability function of the two-parameter exponential distribution. Classical Maximum likelihood and Bayes estimates for one and two parameters and the reliability function are obtained on the basis of progressively type-II censored samples. The inverted gamma conjugate prior density is assumed for the one-parameter case, whereas the joint prior density of the two-parameter case is composed of the inverted gamma and the uniform densities. A comparison between the obtained estimators is made through a Monte Carlo simulation study. A real example is used to illustrate the proposed methods.

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